Saliency Detection using Region-Based Incremental Center-Surround Distance

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1 Saliency Detection using Region-Based Incremental Center-Surround Distance Minwoo Park Corporate Research and Engineering Eastman Kodak Company Rochester, NY, USA Mrityunjay Kumar Corporate Research and Engineering Eastman Kodak Company Rochester, NY, USA Alexander C. Loui Corporate Research and Engineering Eastman Kodak Company Rochester, NY, USA Abstract A new method to detect salient region(s) in images is proposed in this paper. The proposed approach, which is inspired by object-based visual attention theory, segments the input image into coherent regions and measures region-based center-surround distance (RBCSD) using segmented regions and color histograms. Furthermore, segmented regions are merged such that the RBCSD of the merged region is greater than the individual RBCSD of the component regions through region-based incremental distance (RBCSD+I) process. Due to this RBCSD+I process, merged regions may contain incoherent color regions, which improves the robustness of the proposed approach. The key advantages of the proposed algorithm are: (1) it provides a salient region with plausible object boundaries, (2) it is robust to color incoherency present in the salient region, and (3) it is computationally efficient. Extensive qualitative and quantitative evaluation of the proposed algorithm on widely used data sets and comparison with the existing saliency detection approaches clearly indicates the feasibility of the proposed approach. Keywords-Saliency, center-surround distance, image segmentation. I. INTRODUCTION Visual saliency refers to a distinct subjective perceptual quality that makes certain locations or objects of an image stand out from the surroundings. It is well known that the primate visual system processes only the most important or salient information present in the scene using a selective attention mechanism where salient information usually corresponds to location or objects that exhibit high visual saliency [1], [2], [3]. Saliency detection also works on the similar principle and attempts to model salient objects present in input images or videos. Saliency detection is an important research topic and one can find its application in many image processing and computer vision areas such as object detection [4], object tracking [5], video compression [6], and photorealistic animation [7], to name a few. There are two governing theories in visual attention [3], [8]: (i) space-based approach, and (ii) object-based approach. Space-based approach, a time-honored model for saliency detection, attempts to identify visually salient spatial locations. There are a number of computational models that support space-based theory in psychophysics and computer vision literature [1], [2], [9], [10]. The major difference between these models lies in the methods of computing and (a) (b) (c) (d) (e) (f) (g) (h) Figure 1. (a) Input image (b) Segmentation using [16] (c) Saliency map [13] (d) Saliency object detection using [13] (e) Salient object: ground truth (f) Segmentation result: proposed approach (g) Saliency map: proposed approach (h) Salient object detection: proposed approach combining features to generate continuous saliency maps [3]. The goal of the object-based approach is to select salient objects [8] and numerous algorithms that support this approach have been introduced in recent years in psychophysics and computer vision literature [3], [11], [12], [13], [14], [15]. However, saliency computation of these methods is performed independently from a segmentation and a further segmentation (grouping) is performed based on the saliency map computed by the traditional space-based computational model. Our proposed method is also an object-based computational model. However, one fundamental difference from the aforementioned object-based models is that our method performs segmentation (or perceptual grouping) and saliency computation simultaneously in such a way that the saliency represented by the region-based center-surround distance (RBCSD) 1 of the merged regions is higher than that of each corresponding component region. Therefore, our proposed method enables grouping of inhomogeneous regions as long as the merging of the inhomogeneous regions increases the 1 The RBCSD will be explained in detail in Section III.

2 saliency. As can be seen in Figure 1, the head and face of the chicken are grouped as one object even if they have incoherent colors. Extensive qualitative and quantitative evaluations clearly indicate that our proposed method has the state-of-theart performance in terms of the speed and accuracy over the existing methods when evaluated on a public data set introduced by [13]. II. RELATED WORK Prior methods for identifying salient objects in a digital image generally require a computationally intensive search process. Such methods also typically require each salient object to be composed of homogeneous regions. Itt and Koch [1][2] introduced a biologically motivated model for saliency detection. The key steps of their model include (i) computation of low-level features of images (e.g., color contrast, edges, and edge orientations at different scales using up and down sampling), (ii) computation of center surround responses at different scales using differences of Gaussians, and record local maxima responses, and (iii) combining of all the computed responses to generate a saliency map. However, the saliency map does not provide concrete boundaries around salient regions as this algorithm is based on the space-based visual saliency theory. In [9], Hou et al. described a space-based visual saliency algorithm to approximate the innovation part of an image by removing statistically redundant components. This approach involves a high pass filtering operation of log spectral magnitudes and tends to detect small salient objects and edges of a large salient object. The algorithm proposed by Luo et al. in [11] automatically determines main subjects in photographic images. This method provides a measure of belief for the location of main subjects within a digital image and thereby provides an estimate of the relative importance of different subjects in an image. The output of the algorithm is in the form of a list of segmented regions ranked in descending order of their estimated importance. This algorithm first segments an input image and then groups regions into larger segments that correspond to physically coherent objects. A saliency score is then computed for each of the resulting regions and the region that mostly contains the main subject is determined using probabilistic reasoning. However, one of the shortcomings of this approach is that image regions that constitute the main subject must be coherent. For example, if the main subject is a person wearing a red shirt with black pants, the region merging technique in this method will not combine the two regions as it relies on the segmentation algorithm. Liu et al. in [12] proposed a conditional random field learning based framework to combine a set of local, regional, and global features including multiscale contrast, centersurround histogram, and color spatial distribution to detect salient objects. However, a strong limitation of this method is that it requires a range of values for ratio and rectangular sizes to evaluate the center-surround histogram. Luo et al. [17] addresses the limitation of [12] by searching a rectangular region that maximizes the saliency density. However, [17] can neither define the salient object boundary nor compute the salient rectangular boundary simultaneously. Valenti et al. exploited the fact that, to infer visual saliency, salient objects should have local characteristics different from the rest of the scene [15]. They used a novel operator to combine local characteristics such as edges, color, shape, etc. to deduce global information. The resulting global information is used in conjunction with a segmentation algorithm to discriminate salient objects from the background. The algorithm proposed by Achanta et al. [13] produces a full-resolution saliency map with well-defined boundaries for the salient objects. This algorithm first computes a mean color for the entire image and then the mean color is subtracted from each pixel value to produce the saliency map. Then mean-shift segmentation is performed independently to produce the salient region boundary by discarding image segments that have mean saliency values of the segments lower than an adaptive threshold. This approach is not capable of detecting the local salient region if the mean color of the local salient region is similar to that of the entire image. Also it does not have real time performance. Thus, there remains a need for a computationally efficient and robust method to determine object saliency in a digital image that can work with both homogeneous and nonhomogeneous image regions with well-defined salient object boundaries. Our proposed method addresses these problems effectively and determines salient regions with plausible object boundaries. Furthermore, it is robust to the color incoherency of the salient region and is computationally efficient. Figure 2. III. PROPOSED METHOD Overview of the proposed method. An overview of the proposed approach is shown in Figure 2. The Downsample step first downsamples the input image and the Initial Segmentation step segments the downsampled image into coherent regions. Although we use the segmentation method proposed by [16] due to its speed and simplicity, any off-the-shelf segmentation algorithm can be used at this stage to segment downsampled input image. The Compute RBCSD step computes RBCSD for each

3 segmented region produced by the Initial Segmentation step. We call each segmented region as a center region and the rest of the image region as the surrounding region of the corresponding center region, as illustrated in Figure 3(a) 3(d), where c i is the i th center region and s i is the corresponding surround region. Note that each surrounding region is defined with respect to a particular center region; therefore, no two surrounding regions are the same, i.e., s i s j if c i c j. The dissimilarity between the center region c i and the surrounding region s i is denoted as the RBCSD w i and is computed using distance metric based on three dimensional Hue-Saturation-Value (HSV) color histogram. (a) (c) Figure 3. (a) Center region (c i ) (b) s i : surrounding region corresponding to c i (c) center region (c ij ) obtained by merging regions c i and c j (d) s ij : surrounding region corresponding to c ij. Next, Construct Graph step constructs a graph (G = {C, E}) where center region c i is the i th node of the graph and the weight of the edge connecting nodes c i, and c j is the RBCSD of the region obtained by merging c i, and c j. The RBCSD+I Process step applies the proposed RBCSD+I algorithm to the center regions based graph (generated at step Construct Graph ) to produce the saliency map. In particular, RBCSD+I merges neighboring center regions c i and c j if the merge results in a higher RBCSD as compared to the individual RBCSDs for those regions. After merging, both center regions and the surround regions are updated (see Figure 3c 3d). This process is repeated until no center region is available for merging that will increase the RBCSD. Due to this selective merging, RBCSD+I produces a segment map wherein each segment has a saliency value. Therefore, the segment map directly lead to the image segmentation (see Figure 1(f)) and the saliency values directly lead to the saliency map (see Figure (b) (d) 1(g)). Note that RBCSD+I is a complex process and must be implemented efficiently to achieve good accuracy and real-time performance. Note that the saliency map produced by the RBCSD+I Process step is a gray scale image. Therefore, to extract a salient object from the input image, we binarize the saliency map ( Thresholding step) using an adaptive thresholding [13] (see Figure 1(h)). Detailed descriptions of the image segmentation algorithm, computation of RBCSD, graph construction, and RBCSD+I process are provided next. A. Graph-based Segmentation Algorithm As mentioned previously, input image segmentation is the first step of the proposed approach. We use the graph-based segmentation algorithm (GSA) proposed in [16] to perform image segmentation. In addition to image segmentation, as explained later in this paper, we also use a general graph representation and disjoint-set forests data structure used widely for segmentation algorithms to implement our proposed algorithm. Since GSA also uses a graph representation as well as disjoint-set forests data structure and is used as an initialization step to our proposed method, we provide a brief review of GSA for the completeness of the paper and better understanding. Note that other segmentation algorithms can also easily be used in the proposed framework and we selected GSA solely because of its simplicity and computation efficiency. A sample different behavior of our method and GSA in terms of segmentation can be seen in Figures 1(b) and 1(f). In GSA [16], initially, a graph is constructed over the entire image and each pixel p i is treated as a unique segmented region c i and initial edge is defined over eight connected neighborhoods of each pixel p i. The weight of the edge is the Euclidean distance between the color of the edge pixels. Then each edge is sorted in ascending order by edge weight. By iteratively checking each edge in the sorted order, the merging of two nodes c i and c j connected by the edge is performed when a minimum edge weight that connects c i and c j is smaller than minimal internal variation of c i and c j given as MInt(c i, c j ) = min(int(c i ) + k/ c i, Int(c j ) + k/ c j where Int(c i ) is a maximum weight in c i and c i is the number of pixels in c i. The term k/ c i is used to enable initial merging even if the weight of the edge is larger than their internal variations, since the internal variation of an initial node (pixel p) is zero. The parameter k indirectly controls the size of each segment with a larger k, which generally results in larger segments with a higher probability of missing segmentation boundaries. Interested readers are referred to [16] for more details. We first downsample the input image in such a way that max(h, w) = 64., while we preserve the aspect ratio where h and w are the height and width, respectively, of the downsampled image. We segment the downsampled image

4 into a set of N regions where the i th region is represented as c i (1 i N) and the value of k is set to 10. Figure 4. Graph structure with a node for each center region c i and an edge e ij defined between two neighboring center regions c i and c j The weight of the edge between regions c i and c j, w ij (= w ij ) is computed by the center-surround distance of merged region c ij. B. RBCSD Computation We then compute unnormalized three-dimensional HSV color histograms for the entire image and each center region c i (1 i N), which are denoted as A(I) and A(c i ), respectively. Then the HSV color histogram of surrounding region of c i, A(s i ) can be computed efficiently as: A(s i ) = A(I) A(c i ) (1) Similarly, the HSV color histogram for merged regions c i and c j can be computed efficiently as: A(c ij ) = A(c i ) + A(c j ) (2) and the HSV color histogram of the surrounding region of c ij can be computed as: A(s ij ) = A(I) A(c ij ) (3) Next we compute a center-surround distance w i using a normalized cross correlation between the center region c i and the surrounding region s i, which is formally given as: w i = (1 NCC (A(c i ), A(s i )))/ 2 (4) where N CC(A, B) is a normalized cross correlation function between A and B. This formulation enables fast computation that does not require color histogram computation repeatedly during our proposed RBCSD+I process and it is valid for any histogram-based features without loss of generality. Moreover, once A(I) and A(c i ) for 1 i N are computed, any of the actual merging for c i does not need to be performed during the proposed RBCSD+I process since normalized cross-correlation and the HSV color histogram of any merged region and surrounding region can be computed by linear operations on histograms. We use 10 bins for each of H, S, and V channels resulting in 1000 by one color histogram vector. C. Graph Construction Then we build an undirected graph G = (C, E) where c i C is the center regions and e ij E is an edge between neighboring region c i and c j. An example of such a graph, based on Figure 3(a), is shown in Figure 4. For every edge e ij between neighboring center regions c i and c j, its weight w ij is computed as: w ij = [1 NCC (A (c ij ), A (s ij ))] /2 (5) where A (c ij ) and A (s ij ) are computed by equations (2) and (3). Note that weight w ij in the proposed method considers not only the two neighboring regions but also the surrounding region of the two regions while most of the graph-based segmentation algorithm measures the similarity of the neighboring two regions only. Proposed Algorithm 1 Downsample input image //see Figure 2 Downsample step 2 Segment the downsampled image to get c i for 1 i N 3 Set i = 1, m = 1 4 while i N 5 Set p(i) = i // Set parent node of i as i. 6 Compute w i //see Figure 2 Compute RBCSD step 7 i=i+1 8 end 9 Build a graph G = (C, E), c i C, e m E, e m = e ij = (c i, c j, w ij), (1 m K) //see Figure 2 Construct Graph step 10 Sort e m(= e ij) in the order of decreasing w ij 11 while m K // for e m = e ij = (c i, c j, w ij). 12 a=p(i), b=p(j) // find parent node of i and j 13 Compute w ab 14 if w ab > w a and w ab > w b or NCC(c a, c b ) > Merge c a and c b //no actual merging is performed. 16 A(c a) = A(c a) + A(c b ), A(c b ) = A(c a) 17 p(b) = a, w a = w ab, w b = w ab 18 end 19 m=m+1 20 end //see Figure 2 RBCSD+I process step 21 Build saliency map using c p(i) and w p(i) 22 Run adaptive thresholding to binarize the saliency map //see Figure 2 Thresholding step Figure 5. Pseudo code for our proposed algorithm. To implement this algorithm, disjoint-set forests data structure is useful to maintain information about merged region (line 15). D. RBCSD+I Process For the graph G, we sort edge e ij with respect to its weight w ij and start to examine the edge with the highest w ij. The merge of two neighboring regions c i and c j is performed only when the following criterion given by: w ij > w i and w ij > w j or NCC(c i, c j ) > 0.6 (6)

5 is satisfied. The inequality NCC(c i, c j ) > 0.6 enables merging of two similar regions that were not merged by the segmentation algorithm. In Section IV-D, we also report quantitative results by using only the last inequality (RBCSD only) for merge operation to show the effectiveness of the RBCSD+I process. Finally, the entire algorithm is summarized in Figure 5, where RBCSD+I is implemented using the disjoint-set forests data structure. The last line in Figure 5 produces the saliency map using the proposed method. Although the proposed algorithm is a greedy algorithm with respect to merging regions to increase RBCSD, the qualitative and quantitative evaluation of the proposed algorithm on widely used data sets and comparison with the existing saliency detection approaches clearly indicates superior performance of the proposed approach. IV. EXPERIMENT We compare six algorithms on a data set introduced by [13] where the ground truth salient region with a detailed boundary is provided for each of the 1000 images in the database. We denote methods of Itti and Coch [1], Ma and Zhang [18], Harel et al. [19], Hou and Zhang [9], Achanta et al. [14], and Achanta et al. [13] as IT, MZ, GB, SR, AC, and IG, respectively, as used in [13]. B. Salient Region Segmentation by Automatic Thresholding and Image Segmentation We also follow the same comparison method as [13]. Because our proposed method simultaneously segments and detects the salient region, we do not perform segmentation as post-processing as required by all other comparable methods. We simply collect regions of which the saliency value is above the automatic threshold T a to detect the most salient region. The automatic threshold T a is given as: T a = ( 1 N N i=1 w i + 4 max 1 i N w i ) 1 5 The first term in equation (7) is the average RBCSD ( 1 N N i=1 w i) and the second term is the maximum RBCSD (max 1 i N w i ). We set T a given by equation 7 after we tested for 50 images to find out the optimal automatic threshold. However, we found that performance of the proposed method is not sensitive to any value T a between the average RBCSD and the maximum RBCSD. The automatic threshold for other comparable methods is the same as in [13]. As can be seen the quantitative results in Figure 7, our proposed method outperforms all 6 comparable methods in terms of R precision, R recall, and F-measure (= (1+α) R recall R precision α R recall +R precision ) except that ours and IG [13] have about the same R recall. We set α = 0.3 as in [13]. (7) A. Salient Region Segmentation by Fixed Thresholding To obtain the salient region segmentation, a fixed threshold T is applied on the salieny map to generate a binary mask. As can be seen in Figures 1(g) and 9, our saliency map has discrete value since each center region has an uniform saliency (RBCSD) value. We first normalize the saliency map of each algorithm to have saliency values in the range [0, 255], then we vary a threshold T to obtain a binary mask for the salient region. The resulting precision versus recall curve is shown in Figure 6 where the precision rate (R precision ) and recall rate (R recall ) are the ratios of correctly detected salient region (TP) to the detected, and TP to the ground truth salient region, respectively. This curve provides a performance characteristic of each algorithm to detect the salient region. All methods achieve R recall = 100% at T = 0 since entire image is selected as a salient region, resulting the lowest R precision. We use the saliency maps provided by [13] for IT, IG, AC, GB, MZ, and SR. As can be seen in Figure 6, when our proposed method has the minimum R recall of 53%, R precision is 89% and R precision of the state-of-the-art algorithm [13] is 78% when R recall is about the same. Again our minimum R recall of 53% is achieved because each region has uniform RBCSD value. However, the minimum R recall of all other methods is near 0% due to continuous saliency maps. Figure 7. Performance of saliency detection when the mean-shift segmentation is applied to each of the 6 algorithms. The mean-shift clustering is not performed for our proposed method, since the result of our method is direct segmentation of the saliency region. The actual R precision, R recall, and F-measure were provided by the Achanta et al.[13] C. Speed Comparison The typical speed of IG [13] for 300 by 400 images is 3.4 seconds in a modern desktop machine (Intel Dual Core 2.4Ghz, 4GB RAM, unoptimized C++ implemented in serial processing), whereas our method only takes seconds under the same machine while exhibiting better performance characteristic in terms of the precision and recall curve than the state-of-the-art algorithm, IG [13] (see Figure 6).

6 Figure 6. Left: Precision and recall curve with respect to varying threshold T. Note that the recall rate of our method is 53% when the best precision rate is 89% for 1000 images. Overall, GB and AC perform similarly. The discontinuity in the curve for our method exists because the threshold T is varied from 0 to 254 and the most salient region in our saliency map has value of 255 (See our saliency maps in Figure 9). Right: Best performance of each algorithm when it is optimized for the best F-measure with respect to varying threshold T. (a) Input, GT, Ours 1, Ours 2 (b) Input, GT, Ours 1, Ours 2 Figure 10. From the left to the right in each sub figure: input image, ground truth (GT), our result for the most salient region, and our result for multiple salient parts with their saliency ranks are shown, respectively (the more salient region has the lower rank). D. Effect of RBCSD+I To show the effectiveness of the RBCSD+I, we also run the proposed algorithm using only the last test clause in the merging criterion given by equation (6). As can be seen in Figure 8, the method using only the last clause generates a different segmentation, resulting different RBCSD value on each region. Quantitative results for both methods for the same test set are as follows: Rprecision of 81% and 83%, Rrecall of 61% and 75%, and F-meaure of 75% and 79% for RBCSD and RBCSD+I, respectively. V. D ISCUSSION As can be seen in some of the results in Figures 9 and 10, the boundaries of salient regions detected by our proposed method are often coarse. Studies on the biological vision system tell us that more refined vision processing occurs selectively when the image is projected onto the retina. Therefore, we seek to improve boundaries of the salient region as a post processing function. In addition, several different salient regions with numeric numbers can be seen in the rightmost column in Figure 10. The lower number indicates the more salient region. The results reveal the semantically hierarchical structure of objects. For example, as can be seen in the rightmost column in Figure 10a, the most salient region is the post box that contains the second most salient region, the white rectangular label and the post box is supported by the third most salient region, black post. As can be seen in the rightmost column in Figure 10b, the arrow within the diamond is ranked as the most salient region and the diamond is ranked as the second most salient region. This shows our proposed method could be used as a visual feature detector for object detection, video key frame detection, and video summarization. We will explore these areas in the future. VI. C ONCLUSION A method to identify high saliency regions in an image is introduced. Our method first segments the downsampled image then measures the region-based center-surround distance using the segmented region and a color histogram. Next, the merge of each segmented region is performed if the centersurround distance of the merged region is higher than those of each other region through RBCSD+I process. Therefore, the regions are merged and center-surround distance of each resulting region is increased even if the merged region might not have coherent color. The benefits of the proposed method are 1) the method is real-time capable, 2) the output is a salient region with plausible object boundaries, and 3) the method tolerates color incoherency of the salient region. The extensive experiments, both qualitative and quantitative, show that our proposed method has the state-of-the-art

7 (a) Input (b) Segmentation by (c) Segmentation by RBCSD RBCSD+I (d) Ground truth (e) Salient region by RBCSD (f) Salient region by RBCSD+I (g) Input (h) Segmentation by RBCSD (i) Segmentation by RBCSD+I (j) Ground truth (k) Salient region by RBCSD (l) Salient region by RBCSD+I Figure 8. Left column: Input image. Second column: Segmentation by RBCSD. Third column: Segmentation by RBCSD+I. Fourth column: Ground truth salient object. Fifth Column: Salient object by RBCSD. Rightmost column: Salient object by RBCSD+I. performance in terms of speed and accuracy over the existing methods when evaluated on a public data set. ACKNOWLEDGMENT The authors thank R. Achanta and co-workers for providing their experimental results. REFERENCES [1] L. Itti, C. Koch, and E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 2, pp , [2] L. Itti and C. Koch, Computational modelling of visual attention, Nature Reviews. Neuroscience, vol. 2, no. 3, pp , Mar [3] Y. Sun and R. Fisher, Object-based visual attention for computer vision, Artificial Intelligence, vol. 146, pp , May [4] P. Khuwuthyakorn, A. Robles-Kelly, and J. Zhou, Object of interest detection by saliency learning, in 11th European Conference on Computer Vision 2010, pp [5] V. Mahadevan and N. Vasconcelos, Saliency-based discriminant tracking, in IEEE Conference on Computer Vision and Pattern Recognition 2009, pp [6] N. Dhavale and L. Itti, Saliency-based multi-foveated mpeg compression, in Proc. IEEE Seventh International Symposium on Signal Processing and its Applications, Paris, France, Jul 2003, mod;bu;cv, pp [7] L. Itti, N. Dhavale, and F. Pighin, Photorealistic attentionbased gaze animation, in Proc. IEEE International Conference on Multimedia and Expo, Jul 2006, pp [8] G. D. Logan, The code theory of visual attention: an integration of space-based and object-based attention, Psychological Review, vol. 103, no. 4, [9] X. Hou and L. Zhang, Saliency detection: A spectral residual approach, in IEEE Conference on Computer Vision and Pattern Recognition 2007, pp [10] C. Koch and S. Ullman, Shifts in selective visual attention: towards the underlying neural circuitry, Human Neurobiology, vol. 4, no. 4, pp , [11] J. Luo, A. Singhal, S. P. Etz, and R. T. Gray, A computational approach to determination of main subject regions in photographic images, Image and Vision Computing, vol. 22, no. 3, pp , [12] T. Liu, J. Sun, N. ning Zheng, X. Tang, and H. yeung Shum, Learning to detect a salient object, in IEEE Conference on Computer and Vision Pattern Recognition 2007, pp [13] R. Achanta, S. Hemami, F. Estrada, and S. Susstrunk, Frequency-tuned salient region detection, in IEEE Conference on Computer Vision and Pattern Recognition 2009, Jun., pp [14] R. Achanta, F. Estrada, P. Wils, and S. Ssstrunk, Salient region detection and degmentation, in International Conference on Computer Vision Systems 2008, pp [15] R. Valenti, N. Sebe, and T. Gevers, Image saliency by isocentric curvedness and color, in IEEE International Conference on Computer Vision 2009, pp [16] P. F. Felzenszwalb and D. P. Huttenlocher, Efficient graphbased image segmentation, International Journal of Computer Vision, vol. 59, no. 2, pp , [17] Y. Luo, J. Yuan, P. Xue, and Q. Tian, Saliency density maximization for object detection and localization, in Proceedings of the 10th Asian conference on Computer vision Volume Part III, Berlin, Heidelberg, pp [18] Y.-F. Ma and H.-J. Zhang, Contrast-based image attention analysis by using fuzzy growing, in Proceedings of the Eleventh ACM International Conference on Multimedia ACM, 2003, pp [19] J. Harel, C. Koch, and P. Perona, Graph-based visual saliency, in Advances in Neural Information Processing Systems 19, 2007, pp

8 (a) Input, GT, IG [13], Our saliency map, Ours (b) Input, GT, IG [13], Our saliency map, Ours (c) Input, GT, IG [13], Our saliency map, Ours (d) Input, GT, IG [13], Our saliency map, Ours (e) Input, GT, IG [13], Our saliency map, Ours (f) Input, GT, IG [13], Our saliency map, Ours (g) Input, GT, IG [13], Our saliency map, Ours (i) Input, GT, IG [13], Our saliency map, Ours (k) Input, GT, IG [13], Our saliency map, Ours (m) Input, GT, IG [13], Our saliency map, Ours (o) Input, GT, IG [13], Our saliency map, Ours (q) Input, GT, IG [13], Our saliency map, Ours (h) Input, GT, IG [13], Our saliency map, Ours (j) Input, GT, IG [13], Our saliency map, Ours (l) Input, GT, IG [13], Our saliency map, Ours (n) Input, GT, IG [13], Our saliency map, Ours (p) Input, GT, IG [13], Our saliency map, Ours (r) Input, GT, IG [13], Our saliency map, Ours Figure 9. Qualitative comparison to the state-of-the-art algorithm, IG [13]: From left to the right in each sub figure: input image, ground truth (GT), a result of IG[13], our saliency map, and our result are shown, respectively. As can be seen in the figure, our approach detects small and large regions reliably.