nips nips2006 nips2006-8 knowledge-graph by maker-knowledge-mining

8 nips-2006-A Nonparametric Approach to Bottom-Up Visual Saliency

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Author: Wolf Kienzle, Felix A. Wichmann, Matthias O. Franz, Bernhard Schölkopf

Abstract: This paper addresses the bottom-up inﬂuence of local image information on human eye movements. Most existing computational models use a set of biologically plausible linear ﬁlters, e.g., Gabor or Difference-of-Gaussians ﬁlters as a front-end, the outputs of which are nonlinearly combined into a real number that indicates visual saliency. Unfortunately, this requires many design parameters such as the number, type, and size of the front-end ﬁlters, as well as the choice of nonlinearities, weighting and normalization schemes etc., for which biological plausibility cannot always be justiﬁed. As a result, these parameters have to be chosen in a more or less ad hoc way. Here, we propose to learn a visual saliency model directly from human eye movement data. The model is rather simplistic and essentially parameter-free, and therefore contrasts recent developments in the ﬁeld that usually aim at higher prediction rates at the cost of additional parameters and increasing model complexity. Experimental results show that—despite the lack of any biological prior knowledge—our model performs comparably to existing approaches, and in fact learns image features that resemble ﬁndings from several previous studies. In particular, its maximally excitatory stimuli have center-surround structure, similar to receptive ﬁelds in the early human visual system. 1

Reference: text

Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 de Abstract This paper addresses the bottom-up inﬂuence of local image information on human eye movements. [sent-6, score-0.492]

2 Most existing computational models use a set of biologically plausible linear ﬁlters, e. [sent-7, score-0.208]

3 , Gabor or Difference-of-Gaussians ﬁlters as a front-end, the outputs of which are nonlinearly combined into a real number that indicates visual saliency. [sent-9, score-0.267]

4 Here, we propose to learn a visual saliency model directly from human eye movement data. [sent-13, score-1.173]

5 The model is rather simplistic and essentially parameter-free, and therefore contrasts recent developments in the ﬁeld that usually aim at higher prediction rates at the cost of additional parameters and increasing model complexity. [sent-14, score-0.157]

6 Experimental results show that—despite the lack of any biological prior knowledge—our model performs comparably to existing approaches, and in fact learns image features that resemble ﬁndings from several previous studies. [sent-15, score-0.316]

7 In particular, its maximally excitatory stimuli have center-surround structure, similar to receptive ﬁelds in the early human visual system. [sent-16, score-0.671]

8 1 Introduction The human visual system samples images through saccadic eye movements, which rapidly change the point of ﬁxation. [sent-17, score-0.658]

9 , to a system that responds to simple, and often local image features, such as a bright spot in a dark scene. [sent-21, score-0.216]

10 During the past decade, several studies have explored which image features attract eye movements. [sent-22, score-0.452]

11 Parkhurst, Law, and Niebur [13] showed that a saliency map [9], computed by a model similar to the widely used framework by Itti, Koch and Niebur [3, 4], is signiﬁcantly correlated with human ﬁxation patterns. [sent-25, score-0.721]

12 Each of the above models is built on a particular choice of image features that are believed to be relevant to visual saliency. [sent-27, score-0.411]

13 A common approach is to compute several feature maps from linear ﬁlters that are biologically plausible, e. [sent-28, score-0.179]

14 , Difference of Gaussians (DoG) or Gabor ﬁlters, and nonlinearly combine the feature maps into a single saliency map [1, 3, 4, 13, 16, 21]. [sent-30, score-0.738]

15 This makes it straightforward to construct complex models from simple, biologically plausible components. [sent-31, score-0.158]

16 As a consequence, any such model is biased to certain image structure, and therefore discriminates features that might not seem plausible at ﬁrst sight, but may well play a signiﬁcant role. [sent-33, score-0.293]

17 (b) shows the top right image from (a), together with the recorded ﬁxation locations from all 14 subjects. [sent-36, score-0.31]

18 Instead of using a predeﬁned set of feature maps, our saliency model is learned directly from human eye movement data. [sent-44, score-1.112]

19 The model consists of a nonlinear mapping from an image patch to a real value, trained to yield positive outputs on ﬁxated, and negative outputs on randomly selected image patches. [sent-45, score-0.641]

20 The main difference to previous models is that our saliency function is essentially determined by the fact that it maximizes the prediction performance on the observed data. [sent-46, score-0.595]

21 Below, we show that the prediction performance of our model is comparable to that of biologically motivated models. [sent-47, score-0.175]

22 They consist of 200 natural images (1024×768, 8bit grayscale) and 18,065 ﬁxation locations recorded from 14 na¨ve subjects. [sent-50, score-0.231]

23 The subjects freely viewed ı each image for about three seconds on a 19 inch CRT at full screen size and 60cm distance, which corresponds to 37◦ × 27◦ of visual angle. [sent-51, score-0.353]

24 1 Below, we are going to formulate saliency learning as a classiﬁcation problem. [sent-54, score-0.563]

25 As pointed out in [18, 21], care must be taken that no spurious differences in the local image statistics are generated by using different spatial distributions for positive and negative examples. [sent-58, score-0.197]

26 As an example, ﬁxation locations are usually biased towards the center of the image, probably due to the reduced physical effort when looking straight. [sent-59, score-0.197]

27 At the same time, it is known that local image statistics can be correlated with 1 In our initial study [8], these data were preprocessed further. [sent-60, score-0.154]

28 If we sampled background locations uniformly over the image, our system might learn the difference between pixel statistics at the image center and towards the boundary, instead of the desired difference between ﬁxated and non-ﬁxated locations. [sent-69, score-0.532]

29 To avoid this effect, we use the 18,065 ﬁxation locations to generate an equal number of background locations by using the same image coordinates, but with the corresponding image numbers shufﬂed. [sent-71, score-0.598]

30 The proposed model computes saliency based on local image structure. [sent-73, score-0.759]

31 To represent ﬁxations and background locations accordingly, we cut out a square image patch at each location and stored the pixel values in a feature vector xi together with a label yi ∈ {1; −1}, indicating ﬁxation or background. [sent-74, score-0.634]

32 Unfortunately, choosing an appropriate patch size and resolution is not straightforward, as there might be a wide range of reasonable values. [sent-75, score-0.224]

33 To remedy this, we follow the approach proposed in [8], which is a simple compromise between computational tractability and generality: we ﬁx the resolution to 13 × 13 pixels, but leave the patch size d unspeciﬁed, i. [sent-76, score-0.224]

34 For each image location, 11 patches were extracted, with sizes ranging between d = 0. [sent-80, score-0.336]

35 47◦ and d = 27◦ visual angle, equally spaced on a logarithmic scale. [sent-81, score-0.215]

36 Each patch was subsampled to 13 × 13 pixels, after low-pass ﬁltering to reduce aliasing effects. [sent-82, score-0.173]

37 The range of sizes was chosen such that pixels in the smallest patch correspond to image pixels at full screen resolution, and that the largest patch has full screen height. [sent-83, score-0.75]

38 Finally, for each patch we subtracted the mean intensity, and stored the normalized pixel values in a 169-dimensional feature vector xi . [sent-84, score-0.312]

39 This is necessary, since image patches from different locations can overlap, leading to a severe over-estimation of the generalization performance. [sent-93, score-0.458]

40 3 Model and Learning Method From the eye movement described in Section 2, we learn a bottom-up saliency map f (x) : R169 → R using a support vector machine (SVM) [2]. [sent-94, score-0.94]

41 We model saliency as a linear combination of Gaussian radial basis functions (RBFs), centered at the training points xi , m f (x) = αi yi exp − i=1 x − xi 2σ 2 2 . [sent-95, score-0.774]

42 1, this design parameter, as well as the RBF bandwidth σ and the patch size d is determined by maximizing the model’s estimated prediction performance. [sent-100, score-0.205]

43 Similar to most existing approaches, our model is based on linear ﬁlters whose outputs are nonlinearly combined into a real-valued saliency measure. [sent-102, score-0.775]

44 This is a common model for the early visual system, and receptive-ﬁeld estimation techniques such as reverse-correlation usually make the same assumptions. [sent-103, score-0.237]

45 This implies that the system is has no a priori preference for particular image structures. [sent-109, score-0.216]

46 1 Experiments Selection of d, σ, and λ For ﬁxing d, σ, and λ, we conducted an exhaustive search on a 11 × 9 × 13 grid with the grid points equally spaced on a log scale such that d = 0. [sent-131, score-0.173]

47 In order to make the search computationally tractable, we divided the training set (Section 2) into eight parts. [sent-144, score-0.149]

48 2 Due to the subsampling (Section 2), the optimal patch size of d = 5. [sent-163, score-0.173]

49 4◦ leads to an effective saliency map resolution of 89 × 66 (the original image is 1024 × 768), which corresponds to 2. [sent-164, score-0.804]

50 (a) shows a natural scene from our database, together with the recorded eye movements from all 14 subjects. [sent-168, score-0.432]

51 Itti’s saliency map, using ”standard” normalization is shown in (b). [sent-169, score-0.563]

52 The picture in (c) shows our learned saliency map, which was re-built for this example, with the image in (a) excluded from the training data. [sent-171, score-0.813]

53 For a qualitative comparison, Figure 3 shows our learned saliency map and Itti’s model evaluated on a sample image. [sent-202, score-0.693]

54 The main insight here is that our nonparametric model performs at the same level as existing, biologically motivated models, which implement plausible, multi-scale front-end ﬁlters, carefully designed non-linearities, and even global effects. [sent-204, score-0.196]

55 , that it has learned regularities in the data that are relevant to the human ﬁxation selection mechanism. [sent-208, score-0.243]

56 To avoid this, we instead characterize the learned function by means of inputs x that are particularly excitatory or inhibitory to the entire system. [sent-214, score-0.285]

57 As a ﬁrst test, we collected 20, 000 image patches from random locations in natural scenes (not in the training set) and presented them to our system. [sent-215, score-0.582]

58 The top and bottom 100 patches sorted by model output and a histogram over all 20, 000 saliency values are shown in Figure 4 . [sent-216, score-0.863]

59 Note that since our model is unbiased towards any particular image structure, the different Figure 4: Natural image patches ranked by saliency according to our model. [sent-217, score-1.125]

60 The panels (a) and (b) show the bottom and top 100 of 20, 000 patches, respectively (the dots in between denote the 18, 800 patches which are not shown). [sent-218, score-0.182]

61 A histogram of all 20, 000 saliency values is given on the lower right. [sent-219, score-0.609]

62 (b) frequency (x1000) (a) 6 4 2 0 −2 −1 0 1 saliency 2 patterns observed in high and low output patches are solely due to differences between pixel statistics at ﬁxated and background regions. [sent-225, score-0.894]

63 The high output patches seem to have higher contrast, which is in agreement with previous results, e. [sent-226, score-0.241]

64 Another result from [14, 18] is that in natural images the correlation between pixel values decays faster at ﬁxated locations, than at randomly chosen locations. [sent-234, score-0.189]

65 Figure 4 shows this trend as well: as we move away from the patch center, the pixels’ correlation with the center intensity decays faster for patches with high predicted salience. [sent-235, score-0.485]

66 Moreover, a study on bispectra at ﬁxated image locations [10] suggested that “the saccadic selection system avoids image regions, which are dominated by a single oriented structure. [sent-236, score-0.598]

67 A closer look at Figure 4 reveals that our model tends to attribute saliency not alone to contrast, but also to non-trivial image structure. [sent-238, score-0.759]

68 To further characterize the system, we explicitly computed the maximally excitatory and inhibitory stimuli. [sent-242, score-0.347]

69 The initial x were constructed by drawing 169 pixel values from a normal distribution with zero mean and then normalizing the patch standard deviation to 0. [sent-248, score-0.246]

70 We also re-ran this experiment with natural image patches as starting values, with identical results. [sent-259, score-0.372]

71 This indicates that our saliency function has essentially two minima and two maxima in x. [sent-260, score-0.631]

72 The ﬁrst two images (a) and (b) show the maximally inhibitory stimuli. [sent-262, score-0.258]

73 5 (a) (b) (c) (d) Figure 5: Maximally inhibitory and excitatory stimuli of the learned model. [sent-267, score-0.409]

74 Note the large magnitude of the saliency values compared to the typical model output (cf. [sent-268, score-0.635]

75 (a) and (b): the two maximally inhibitory stimuli (lowest possible saliency). [sent-270, score-0.343]

76 (c) and (d): the two maximally excitatory stimuli (highest possible saliency), (e) and (f): the radial average of (c) and (d), respectively. [sent-271, score-0.427]

77 On the other hand, the maximally excitatory stimuli, denoted by (c) and (d), have center-surround structure. [sent-280, score-0.242]

78 All four stimuli have zero mean, which is not surprising since during gradient search, both the initial value and the step directions—which are linear combinations of the training data—have zero mean. [sent-281, score-0.168]

79 3 The optimal stimuli thus bear a close resemblance to receptive ﬁelds in the early visual system [11]. [sent-286, score-0.446]

80 To see that the optimal stimuli have in fact prototype character, note how the histogram in Figure 4 reﬂects the typical distribution of natural image patches along the learned saliency function. [sent-287, score-1.157]

81 It illustrates that the saliency values of unseen natural image patches usually lie between −2. [sent-288, score-0.935]

82 In contrast, our optimal stimuli have saliency values of 5. [sent-293, score-0.687]

83 5, indicating that they represent the difference between ﬁxated and background locations in a much more articulated way than any of the noisy measurements in our data set. [sent-295, score-0.168]

84 5 Discussion We have presented a nonparametric model for bottom-up visual saliency, trained on human eye movement data. [sent-296, score-0.663]

85 Nevertheless, we found that the prediction performance of our system is comparable to that of parametric, biologically motivated models. [sent-301, score-0.195]

86 Also, we found that the maximally excitatory stimuli of our system have centersurround structure, similar to DoG ﬁlters commonly used in early vision models [3, 13, 21]. [sent-307, score-0.538]

87 This is a nontrivial result, since our model has no preference for any particular image features, i. [sent-308, score-0.196]

88 , a priori, any 13 × 13 image patch is equally likely to be an optimal stimulus. [sent-310, score-0.365]

89 Recently, several authors have explored whether oriented (Gabor) or center-surround (DoG) features are more relevant to human eye movements. [sent-311, score-0.411]

90 3 Please note that the radial average curves in Figure 5 (e) and (f) do not necessarily sum to zero, since the patch area in (c) and (d) grows quadratically with its corresponding radius. [sent-314, score-0.234]

91 Quantifying the contribution of low-level saliency to human eye movements in dynamic scenes. [sent-330, score-0.945]

92 Quantitative modeling of perceptual salience at human eye position. [sent-334, score-0.386]

93 A model of saliency-based visual attention for rapid scene analysis. [sent-340, score-0.249]

94 A saliency-based search mechanism for overt and covert shifts of visual attention. [sent-345, score-0.222]

95 Learning an interest operao tor from human eye movements. [sent-361, score-0.338]

96 Shifts in selective visual attention: towards the underlying neural circuitry. [sent-366, score-0.177]

97 The relationship between the locations of spatial features and those of ﬁxations made during visual examination of brieﬂy presented images. [sent-388, score-0.352]

98 Modeling the role of salience in the allocation of overt visual attention. [sent-395, score-0.269]

99 Algorithms for deﬁning visual regions-of-interest: Comparison with eye ﬁxations. [sent-416, score-0.405]

100 Contrast statistics for foveated visual systems: Fixation selection by minimizing contrast entropy. [sent-426, score-0.231]

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