Global Contrast based Salient Region Detection(CVPR 2011)

Global Contrast based Salient Region Detection

Ming-Ming Cheng1Guo-Xin Zhang1Niloy J.Mitra2Xiaolei Huang3Shi-Min Hu1 1TNList,Tsinghua University2KAUST3Lehigh University

Abstract

Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their con-tent,and thus remains an important step in many computer vision tasks including image segmentation,object recog-nition,and adaptive compression.We propose a regional contrast based saliency extraction algorithm,which simul-taneously evaluates global contrast differences and spatial coherence.The proposed algorithm is simple,ef?cient,and yields full resolution saliency maps.Our algorithm con-sistently outperformed existing saliency detection methods, yielding higher precision and better recall rates,when eval-uated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for sub-sequent image processing.

1.Introduction

Humans routinely and effortlessly judge the importance of image regions,and focus attention on important parts. Computationally detecting such salient image regions re-mains a signi?cant goal,as it allows preferential allocation of computational resources in subsequent image analysis and synthesis.Extracted saliency maps are widely used in many computer vision applications including object-of-interest image segmentation[12,17],object recogni-tion[24],adaptive compression of images[6],content-aware image resizing[4,30],and image retrieval[5].

Saliency originates from visual uniqueness,unpre-dictability,rarity,or surprise,and is often attributed to vari-ations in image attributes like color,gradient,edges,and boundaries.Visual saliency,being closely related to how we perceive and process visual stimuli,is investigated by mul-tiple disciplines including cognitive psychology[25,27], neurobiology[8,21],and computer vision[16,2].Theo-ries of human attention hypothesize that the human vision system only processes parts of an image in detail,while leaving others nearly unprocessed.Early work by Treis-man and Gelade[26],Koch and Ullman[18],and subse-quent attention theories proposed by Itti,Wolfe and

others,

Figure1.Given input images(top),a global contrast analysis is used to compute high resolution saliency maps(middle),which can be used to produce masks(bottom)around regions of interest.

suggest two stages of visual attention:fast,pre-attentive, bottom-up,data driven saliency extraction;and slower,task dependent,top-down,goal driven saliency extraction.

We focus on bottom-up data driven saliency detection using image contrast.It is widely believed that human cor-tical cells may be hard wired to preferentially respond to high contrast stimulus in their receptive?elds[22].We pro-pose contrast analysis for extracting high-resolution,full-?eld saliency maps based on the following observations:

?A global contrast based method,which separates a

large-scale object from its surroundings,is preferred

over local contrast based methods producing high

saliency values at or near object edges.

?Global considerations enable assignment of compara-

ble saliency values to similar image regions,and can

uniformly highlight entire objects.

?Saliency of a region depends on its contrast to the

nearby regions,while contrasts to distant regions are

less signi?cant.

?Saliency maps should be fast and easy to generate to

allow processing of large image collections,and facil-

itate ef?cient image classi?cation and retrieval.

We propose a histogram-based contrast method(HC)to measure saliency.HC-maps assign pixel-wise saliency val-ues based simply on color separation from all other image pixels to produce full resolution saliency maps.We use 1

(a)original(b)IT[16](c)MZ[20](d)GB[13](e)SR[14](f)AC[1](g)CA[11](h)FT[2](i)LC[29](j)HC(k)RC Figure2.Saliency maps computed by different state-of-the-art methods(b-i),and with our proposed HC(j)and RC methods(k).Most results highlight edges,or are of low resolution.See also Figure6(and project webpage).

a histogram-based approach for ef?cient processing,while

employing a smoothing procedure to control quantization

artifacts.Note that our algorithm is targeted towards natu-

ral scenes,and maybe suboptimal for extracting saliency of

highly textured scenes(see Figure12).

As an improvement over HC-maps,we incorporate spa-

tial relations to produce region-based contrast(RC)maps

where we?rst segment the input image into regions,and

then assign saliency values to regions.The saliency value

of a region is now calculated using a global contrast score,

which is measured using the region’s contrast and spatial

distances to other regions in the image.

We have extensively evaluated our methods on publicly

available benchmark data sets,and compared our methods

to(eight)state-of-the-art saliency methods[16,20,29,13,

14,1,2,11]as well as to manually produced ground truth

annotations1.The experiments show signi?cant improve-

ments over previous methods both in precision and recall

rates.Overall,compared to HC-maps,RC-maps produce

better precision and recall,but at the cost of increased com-

putations.Encouragingly,we observe that the saliency cuts

extracted using our saliency maps are,in most cases,com-

parable to manual annotations.We also present applica-

tions of the extracted saliency maps to segmentation,con-

text aware resizing,and non-photo realistic rendering.

2.Related Works

We focus on relevant literature targeting pre-attentive

bottom-up saliency detection,which may be biologically

motivated,or purely computational,or involve both aspects.

Such methods utilize low-level processing to determine the

contrast of image regions relative to their surroundings,us-

ing feature attributes such as intensity,color,and edges[2].

We broadly classify the algorithms into local and global

schemes.

Local contrast based methods investigate the rarity of

image regions with respect to(small)local neighbor-

hoods.Based on the highly in?uential biologically inspired

early representation model introduced by Koch and Ull-

man[18],Itti et al.[16]de?ne image saliency using central-

surrounded differences across multi-scale image features.

Ma and Zhang[20]propose an alternative local contrast

analysis for generating saliency maps,which are then ex-

1Results for1000images and prototype software are available at the

project webpage:https://www.sodocs.net/doc/9914814227.html,/people/%7Ecmm/saliency/

tended using a fuzzy growth model.Harel et al.[13]nor-

malize Itti and colleagues’feature maps to highlight con-

spicuous parts and admit combination with other impor-

tance maps.Liu et al.[19]?nd multi-scale contrast by

linearly combining contrast in a Gaussian image pyramid.

More recently,Goferman et al.[11]simultaneously model

local low-level clues,global considerations,visual organi-

zation rules,and high-level features to highlight salient ob-

jects along with their contexts.Such methods using local

contrast tend to produce higher saliency values near edges

instead of uniformly highlighting salient objects(see Fig-

ure2).

Global contrast based methods evaluate saliency of an

image region using its contrast with respect to the entire

image.Zhai and Shah[29]de?ne pixel-level saliency by

contrast to all other pixels.However,for ef?ciency they

use only luminance information,thus ignoring distinctive-

ness clues in other channels.Achanta et al.[2]propose a

frequency tuned method that directly de?nes pixel saliency

using the color differences from the average image color.

The elegant approach,however,only considers?rst order

average color,which can be insuf?cient to analyze compli-

cated variations common in natural images.In Figures6

and7,we show qualitative and quantitative weaknesses of

such approaches.Furthermore,these methods ignore spa-

tial relationships across image parts,which can be critical

for reliable and coherent saliency detection(see Section5).

3.Histogram Based Contrast

Based on the observation from biological vision that the

vision system is sensitive to contrast in visual signal,we

propose a histogram-based contrast(HC)method to de?ne

saliency values for image pixels using color statistics of the

input image.Speci?cally,the saliency of a pixel is de?ned

using its color contrast to all other pixels in the image,i.e.,

the saliency value of a pixel I k in image I is de?ned as,

S(I k)=

?I i∈I

D(I k,I i),(1)

where D(I k,I i)is the color distance metric between pixels

I k and I i in the L?a?b?space(see also[29]).Equation1

can be expanded by pixel order to have the following form,

S(I k)=D(I k,I1)+D(I k,I2)+···+D(I k,I N),(2)

f r e q u e n c

y

Figure 3.Given an input image (left),we compute its color his-togram (middle).Corresponding histogram bin colors are shown in the lower bar.The quantized image (right)uses only 43his-togram bin colors and still retains suf?cient visual quality for saliency detection.

where N is the number of pixels in image I .It is easy to see that pixels with the same color value have the same saliency value under this de?nition,since the measure is oblivious to spatial relations.Hence,rearranging Equation 2such that the terms with the same color value c j are grouped together,we get saliency value for each color as,

S (I k )=S (c l )=

n j =1

f j D (c l ,c j ),(3)

where c l is the color value of pixel I k ,n is the number of different pixel colors,and f j is the frequency of pixel color c j in image I .Note that in order to prevent salient region color statistics from being corrupted by similar colors from other regions,one can develop a similar scheme using vary-ing window masks.However,given the strict ef?ciency re-quirement,we take the simple global approach.

Histogram based speed up.Naively evaluating the saliency value for each image pixel using Equation 1takes O (N 2)time,which is computationally too expensive even for medium sized images.The equivalent representation in Equation 3,however,takes O (N )+O (n 2)time,implying that computational ef?ciency can be improved to O (N )if O (n 2)≤O (N ).Thus,the key to speedup is to reduce the number of pixel colors in the image.However,the true-color space contains 2563possible colors,which is typically larger than the number of image pixels.

Zhai and Shah [29]reduce the number of colors,n ,by only using luminance.In this way,n 2=2562(typically 2562 N ).However,their method has the disadvantage that the distinctiveness of color information is ignored.In this work,we use the full color space instead of luminance only.To reduce the number of colors needed to consider,we ?rst quantize each color channel to have 12different values,which reduces the number of colors to 123=1728.Con-sidering that color in a natural image typically covers only a small portion of the full color space,we further reduce the number of colors by ignoring less frequently occurring colors.By choosing more frequently occurring colors and ensuring these colors cover the colors of more than 95%of the image pixels,we typically are left with around n =

85

Figure 4.Saliency of each color,normalized to the range [0,1],be-fore (left)and after (right)color space smoothing.Corresponding saliency maps are shown in the respective insets.

colors (see Section 5for experimental details).The colors

of the remaining pixels,which comprise fewer than 5%of the image pixels,are replaced by the closest colors in the histogram.A typical example of such quantization is shown in Figure 3.Note that again due to ef?ciency requirements we select the simple histogram based quantization instead of optimizing for an image speci?c color palette.

Color space smoothing.Although we can ef?ciently compute color contrast by building a compact color his-togram using color quantization and choosing more fre-quent colors,the quantization itself may introduce artifacts.Some similar colors may be quantized to different values.In order to reduce noisy saliency results caused by such randomness,we use a smoothing procedure to re?ne the saliency value for each color.We replace the saliency value of each color by the weighted average of the saliency val-ues of similar colors (measured by L ?a ?b ?distance).This is actually a smoothing process in the color feature space.Typically we choose m =n/4nearest colors to re?ne the saliency value of color c by,

S

(c )=

1

(m ?1)T m i =1(T ?D (c,c i ))S (c i )(4)

where,T = m

i =1D (c,c i )is the sum of distances between color c and its m nearest neighbors c i ,and the normaliza-tion factor comes from m

i =1(T ?D (c,c i ))=(m ?1)T.Note that we use a linearly-varying smoothing weight (T ?D (c,c i ))to assign larger weights to colors closer to c in the color feature space.In our experiments,we found that such linearly-varying weights are better than Gaussian weights,which fall off too sharply.Figure 4shows the typical ef-fect of color space smoothing with the corresponding his-tograms sorted by decreasing saliency values.Note that similar histogram bins are closer to each other after such a smoothing,indicating that similar colors have higher like-lihood of being assigned similar saliency values,thus reduc-ing quantization artifacts (see Figure 7).

Implementation details.To quantize the color space into 123different colors,we uniformly divide each color chan-nel into 12different levels.While the quanti?cation of col-ors is performed in the RGB color space,we measure color

Figure5.Image regions generated by Felzenszwalb and Hutten-

locher’s segmentation method[10](left),region contrast based

segmentation with(left-middle)and without(right-middle)dis-

tance weighting.Incorporating the spatial context,we get a high

quality saliency cut(right)comparable to human labeled ground

truth.

differences in the L?a?b?color space because of its percep-

tual accuracy.However,we do not perform quantization

directly in the L?a?b?color space since not all colors in the

range L?∈[0,100],and a?,b?∈[?127,127]necessar-

ily correspond to real colors.Experimentally we observed

worse quantization artifacts using direct L?a?b?color space

quantization.Best results were obtained by quantiza-

tion in the RGB space while measuring distance in the

L?a?b?color space,as opposed to performing both quanti-

zation and distance calculation in either a single color space,

RGB or L?a?b?.

4.Region Based Contrast

Humans pay more attention to those image regions that

contrast strongly with their surroundings[9].Besides con-

trast,spatial relationships play an important role in human

attention.High contrast to its surrounding regions is usually

stronger evidence for saliency of a region than high contrast

to far-away regions.Since directly introducing spatial rela-

tionships when computing pixel-level contrast is computa-

tionally expensive,we introduce a contrast analysis method,

region contrast(RC),so as to integrate spatial relationships

into region-level contrast computation.In RC,we?rst seg-

ment the input image into regions,then compute color con-

trast at the region level,and de?ne the saliency for each

region as the weighted sum of the region’s contrasts to all

other regions in the image.The weights are set according

to the spatial distances with farther regions being assigned

smaller weights.

Region contrast by sparse histogram comparison.We

?rst segment the input image into regions using a graph-

based image segmentation method[10].Then we build the

color histogram for each region as in Section3.For a region

r k,we compute its saliency value by measuring its color

contrast to all other regions in the image,

S(r k)=

r k=r i

w(r i)D r(r k,r i),(5)

where w(r i)is the weight of region r i and D r(·,·)is the

color distance metric between the two regions.Here we

use the number of pixels in r i as w(r i)to emphasize color

contrast to bigger regions.The color distance between two

regions r1and r2is de?ned as,

D r(r1,r2)=

n1

i=1

n2

j=1

f(c1,i)f(c2,j)D(c1,i,c2,j)(6)

where f(c k,i)is the frequency of the i-th color c k,i among

all n k colors in the k-th region r k with k={1,2}.Note that

we use the frequency of a color occurring in the region as

the weight for this color to re?ect more the color differences

between dominant colors.

Storing and calculating the regular matrix format his-

togram for each region is inef?cient since each region typ-

ically contains a small number of colors in the color his-

togram of the whole image.Instead,we use a sparse his-

togram representation for ef?cient storage and computation.

Spatially weighted region contrast.We further incorpo-

rate spatial information by introducing a spatial weighting

term in Equation5to increase the effects of closer regions

and decrease the effects of farther regions.Speci?cally,for

any region r k,the spatially weighted region contrast based

saliency is de?ned as:

S(r k)=

r k=r i

exp(?D s(r k,r i)/σ2s)w(r i)D r(r k,r i)(7)

where,D s(r k,r i)is the spatial distance between regions r k

and r i,andσs controls the strength of spatial weighting.

Larger values ofσs reduce the effect of spatial weighting

so contrast to farther regions would contribute more to the

saliency of the current region.The spatial distance between

two regions is de?ned as the Euclidean distance between the

centroids of the respective regions.In our implementation,

we useσ2s=0.4with pixel coordinates normalized to[0,1].

5.Experimental Comparisons

We have evaluated the results of our approach on the

publicly available database provided by Achanta et al.[2].

To the best of our knowledge,the database is the largest

of its kind,and has ground truth in the form of accu-

rate human-marked labels for salient regions.We com-

pared the proposed global contrast based methods with8

state-of-the-art saliency detection methods.Following[2],

we selected these methods according to:number of cita-

tions(IT[16]and SR[14]),recency(GB[13],SR,AC[1],

FT[2]and CA[11]),variety(IT is biologically-motivated,

MZ[20]is purely computational,GB is hybrid,SR works

in the frequency domain,AC and FT output full resolution

saliency maps),and being related to our approach(LC[29]).

(a)original (b)LC (c)CA (d)FT (e)HC-maps (f)RC-maps (g)RCC

Figure 6.Visual comparison of saliency maps.(a)original images,saliency maps produced using (b)Zhai and Shah [29],(c)Goferman et al.[11],(d)Achanta et al.[2],(e)our HC and (f)RC methods,and (g)RC-based saliency cut results.Our methods generate uniformly highlighted salient regions (see project webpage for all results on the full benchmark dataset).

We used our methods and the others to compute saliency maps for all the 1000images in the database.Table 1com-pares the average time taken by each method.Our algo-rithms,HC and RC,are implemented in C++.For the other methods namely IT,GB,SR,FT and CA,we used the au-thors’implementations,while for LC,we implemented the algorithm in C++since we could not ?nd the authors’im-plementation.For typical natural images,our HC method needs O (N )computation time and is suf?ciently ef?cient for real-time applications.In contrast,our RC variant is slower as it requires image segmentation [10],but produces superior quality saliency maps.

In order to comprehensively evaluate the accuracy of our methods for salient object segmentation,we performed two experiments using different objective comparison measures.In the ?rst experiment,to segment salient objects and cal-culate precision and recall curves [14],we binarized the saliency map using each possible ?xed threshold,similar to the ?xed thresholding experiment in [2].In the second experiment,we segment salient objects by iteratively ap-plying GrabCut [23]initialized using thresholded saliency maps,as described later.We also use the obtained saliency maps as importance weighting for content aware image re-sizing and non-photo realistic rendering.

Segmentation by ?xed thresholding.The simplest way to get a binary segmentation of salient objects is to thresh-old the saliency map with a threshold T f ∈[0,255].To reli-ably compare how well various saliency detection methods highlight salient regions in images,we vary the threshold T f from 0to 255.Figure 7shows the resulting precision vs.recall curves.We also present the bene?ts of adding the color space smoothing and spatial weighting schemes,along with objective comparison with other saliency extrac-tion methods.Visual comparison of saliency maps obtained by the various methods can be seen in Figures 2and 6.

The precision and recall curves clearly show that our methods outperform the other eight methods.The extrem-ities of the precision vs.recall curve are interesting:At maximum recall where T f =0,all pixels are retained as positives,i.e.,considered to be foreground,so all the meth-ods have the same precision and recall values;precision 0.2and recall 1.0at this point indicate that,on average,there are 20%image pixels belonging to the ground truth salient regions.At the other end,the minimum recall values of our methods are higher than those of the other methods,because the saliency maps computed by our methods are smoother and contain more pixels with the saliency value 255.

Method IT[Time(s)0.611Code Matlab

Table 1.Average time taken in the database (see project webpage)have resolution 400×300.Algorithms were tested using an Dual Core 2.6GHz machine with 2GB RAM.

0.6

0.81Recall P r e c i s i o n IT

0.2

0.40.60.80.60.81Recall P r e c i s i o n

GB Figure 7.Precision-recall curve for naive thresholding of saliency maps using 1000publicly available benchmark images.(Left,mid-dle)Different options of our method compared to GB[13],MZ[20],FT[2],IT[16],SR[14],AC[1],CA[11],and LC[29].NHC denotes naive version of our HC method with color space smoothing disabled,and NRC denotes our RC method with spatial related weighting disabled.(Right)Precision-recall bars for our saliency cut algorithm using different saliency maps as initialization.Our method RC shows high precision,recall,and F βvalues over the 1000image database.(Please refer to project webpage for respective result images.)

Saliency cut.We now consider the use of the com-puted saliency map to assist in salient object segmentation.Saliency maps have been previously employed for unsuper-vised object segmentation:Ma and Zhang [20]?nd rect-angular salient regions by fuzzy region growing on their saliency maps.Ko and Nam [17]select salient regions us-ing a support vector machine trained on image segment fea-tures,and then cluster these regions to extract salient ob-jects.Han et al.[12]model color,texture,and edge features in a Markov random ?eld framework to grow salient ob-ject regions from seed values in the saliency maps.More recently,Achanta et al.[2]average saliency values within image segments produced by mean-shift segmentation,and then ?nd salient objects by identifying image segments that have average saliency above an threshold that is set to be twice the mean saliency value of the entire image.

In our approach,we iteratively apply GrabCut [23]to

Figure 8.Saliency Cut.(Left to right)Initial segmentation,trimap after ?rst iteration,trimap after second iteration,?nal segmenta-tion,and manually labeled ground truth.In the segmented images,blue is foreground,gray is background,while in the trimaps,the foreground is red,the background is green,and unknown regions are left unchanged.

re?ne the segmentation result initially obtained by thresh-olding the saliency map (see Figure 8).Instead of manually inputting a rectangular region to initialize the process,as in classical GrabCut,we automatically initialize GrabCut us-ing a segmentation obtained by binarizing the saliency map using a ?xed threshold,chosen empirically to be the thresh-old that gives 95%recall rate in our ?xed thresholding ex-periments.

Once initialized,we iteratively run GrabCut to improve the saliency cut result (at most 4iterations in our experi-ments).After each iteration,we use dilation and erosion operations on the current segmentation result to get a new trimap for the next GrabCut iteration.As shown in Figure 8,the region outside the dilated region is set to background,the region inside the eroded region is set to foreground,and the remaining areas are set to unknown in the trimap.Grab-Cut,which by itself is an iterative process using Gaussian mixture models and graph cut,helps to re?ne salient object regions at each step.Regions closer to an initial salient ob-ject region are more likely to be part of that salient object than far-away regions.Thus,our new initialization enables GrabCut to include nearby salient object regions,and ex-clude non-salient regions according to color feature dissim-ilarity.In the implementation,we set a narrow border region (15pixels wide)to be always in the background in order to avoid slow convergence in the border region.

Figure 8shows two examples of our saliency cut algo-rithm.In the ?ag example,unwanted regions are correctly excluded during GrabCut iterations.In the ?ower exam-ple,our saliency cut method successfully expanded the ini-

(a)original (b)LC[29](c)CA[11](d)FT[2](e)HC-maps (f)RC-maps (g)ground truth

Figure 9.Saliency cut using different saliency maps for initialization.Related saliency maps are shown in Figure 6.

tial salient regions (obtained directly from the saliency map)and converged to an accurate segmentation result.

To objectively evaluate our new saliency cut method us-ing our RC-map as initialization,we compare our results with results obtained by coupling iterative GrabCut with ini-tialization from saliency maps computed by other methods.For consistency,we binarize each such saliency map using a threshold that gives 95%recall rate in the corresponding ?xed thresholding experiment (see Figure 7).A visual com-parison of the results is shown in Figure 9.Average preci-sion,recall,and F -Measure are compared over the entire ground-truth database [2],with the F -Measure de?ned de-?ned as:

F β=

(1+β2)P recision ×Recall

β×P recision +Recall

.(8)

We use β2=0.3as suggested in Achanta et al.[2]to weight precision more than recall.As can be seen from the com-parison (see Figures 7-right and 9),saliency cut using our RC and HC saliency maps signi?cantly outperform other https://www.sodocs.net/doc/9914814227.html,pared to the state-of-the-art results on this database by Achanta et al.(precision =75%and recall =83%),we achieved better accuracy (precision =90%and recall =90%)(demo software available at the project web-page.)

Content aware image resizing.In image re-targeting,saliency maps are usually used to specify relative impor-tance across image parts (see also [3]).We experimented with using our saliency maps in the image resizing method proposed by Zhang et al.[30],which distributes distortion energy to relatively non-salient regions of an image while preserving both global and local image features (using pub-licly available authors’implementation).Figure 10com-pares the resizing results using our RC-maps with the results using CA[11]saliency maps.Our RC saliency maps help produce better resizing results since the salient object re-gions are piece-wise smooth,which is important for

energy

original CA RC original CA RC Figure https://www.sodocs.net/doc/9914814227.html,parison of content aware image resizing [30]re-sults using CA[11]saliency maps and our RC saliency

maps.

Figure 11.(Middle,right)FT[2]and RC saliency maps are used respectively for stylized rendering [15]of an input image (left).Our method produces a better saliency map,see insets,resulting in improved preservation of details,e.g.,around the head and the fence

regions.

Figure 12.Challenging examples for our histogram based meth-ods involve non-salient regions with similar colors as the salient parts (top),or an image with textured background (bottom).(Left to right)Input image,HC-map,HC saliency cut,RC-map,RC saliency cut.

based resizing methods.CA saliency maps having higher saliency values at object boundaries are less suitable for ap-plications like resizing,which require entire salient objects to be uniformly highlighted.

Non-photorealistic rendering.Artists often abstract im-ages and highlight meaningful parts of an image while masking out unimportant regions [28].Inspired by this ob-servation,a number of non-photorealistic rendering (NPR)efforts use saliency maps to generate interesting effects [7].We experimentally compared our work with the most re-lated,state-of-the-art saliency detection algorithm [2]in the context of a recent NPR technique [15](see Figure 11).Our RC-maps give better saliency masks,which help the NPR method to better preserve details in important image parts and region boundaries,while smoothing out others.

6.Conclusion and Future Works

We presented global contrast based saliency computa-tion methods,namely Histogram based Contrast (HC)and spatial information-enhanced Region based Contrast (RC).

While the HC method is fast and generates results with ?ne details,the RC method generates spatially consistent high quality saliency maps at the cost of reduced computa-tional ef?ciency.We evaluated our methods on the largest publicly available data set and compared our scheme with eight other state-of-the-art methods.Experiments indicate the proposed schemes to be superior in terms of both preci-sion and recall,while still being simple and ef?cient.

In the future,we plan to investigate ef?cient algorithms that incorporate spatial relationships in saliency computa-tion while preserving?ne details in the resulting saliency maps.Also,it is desirable to develop saliency detection algorithms to handle cluttered and textured background, which can introduce artifacts to our global histogram based approach(although we did not encounter such images in the database).Finally,it may be bene?cial to incorporate high level factors like human faces,symmetry to saliency maps. We believe the proposed saliency maps can be used for ef?-cient object detection,reliable image classi?cation,leading to improved image retrieval.

Acknowledgements.This research was supported by the National Basic Research Project of China(NO. 2011CB302205),the Natural Science Foundation of China(NO.U0735001)and the National High Technol-ogy Research and Development Program of China(NO. 2009AA01Z327).

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Unit 4 Global warming全球变暖 一、词汇 about发生；造成 注意：（1）come about是不及物动词短语，不能用于被动语态，常指情况不受人控制的突然发生。有时用it作形式主语，that从句作真正主语。 （2）表示“发生”的词或短语有：happen,occur,take place,break ① Many a quarrel comes about through a misunderstanding. ② The moon came out from behind the clouds. ③ I’ll let you know if anything comes up. ④ I’ll come over and see how you are coming along. ⑤ I came across an old friend yesterday. ⑥ When she came to, she couldn’t recognize the surroundings. ① I subscribe to your suggestion. ② Which magazine do you subscribe to? ③ He subscribed his name to the paper（文件）. ④ He subscribed a large sum to the poor students. n.量；数量

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One of the effects of global warming is the destruction of many important ecosystems.Changing and erratic climate conditions will put our ecosystems to the test, the increase in carbon dioxide will increase the problem. The evidence is clear that global warming and climate change affects physical and biological systems. There will be effects to land, water, and life. Already today, scientists are seeing the effects of global warming on coral reefs, many have been bleached and have died. This is due to warmer ocean waters, and to the fact that some species of plants and animals are simply migrating to better suited geographical regions where water temperatures are more suitable. Melting ice sheets are also making some animals migrate to better regions. This effects the ecosystems in which these plants and animals live. Several climate models have been made and they predict more floods (big floods), drought, wildfires, ocean acidification, and the eventual collapse of many ecosystems throughout the world both on land and at sea. There have been forecasts of things like famine, war, and social unrest, in our days ahead. These are the types of effects global warming could have on our planet. Another important effect that global warming will bring is the loss and endangerment of many species. Did you know that 30 percent of all plant and animal species alive in the world today are at risk of extinction by the year 2050 if average temperatures rise more than 2 to 11.5 degrees Fahrenheit. These mass extinctions will be due to a loss of habitat through desertification, deforestation, and ocean warming. Many plants and animals will also be affected by the inability to adapt to our climate warming.

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