This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM). The proposed framework is inspired by two observations, (1) the contextual information of a scene along with low-level visual cues modulates attention, (2) the influence of scene memorability on eye movement patterns caused by the resemblance of a scene to a former visual experience. Motivated by such observations, we develop a framework that estimates the saliency of a given image using an ensemble of extreme learners, each trained on an image similar to the input image. That is, after retrieving a set of similar images for a given image, a saliency predictor is learnt from each of the images in the retrieved image set using an ELM, resulting in an ensemble. The saliency of the given image is then measured in terms of the mean of predicted saliency value by the ensembles members.
Learning-based techniques are a large group of methods which are establishing a relation between a feature space and human fixations. For example, @cite_44 uses a nonlinear transformation to associate image patches with human eye movement statistics. @cite_60 , a linear SVM classifier is used to establish a relation between three channels of low- (intensity, color, etc), mid- (horizon line) and high-level (faces and people) features and human eye movements in order to produce a saliency map. In a similar vein, @cite_1 employs multiple-instance learning. By learning a classifier, @cite_23 @cite_21 estimate the optimal weights for fusing several conspicuity maps from observers' eye movement data. These approaches often learn a probabilistic classifier to determine the probability of a feature being salient. Then, they employ the estimated saliency probability in order to build a saliency map.
@cite_60 @cite_21 @cite_1 @cite_44 @cite_23
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For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations. Most saliency approaches are based on bottom-up computation that does not consider top-down image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features. This large database of eye tracking data is publicly available with this paper. To predict where subjects look under natural viewing conditions, biologically inspired saliency models decompose visual input into a set of feature maps across spatial scales. The output of these feature maps are summed to yield the final saliency map. We studied the integration of bottom-up feature maps across multiple spatial scales by using eye movement data from four recent eye tracking datasets. We use AdaBoost as the central computational module that takes into account feature selection, thresholding, weight assignment, and integration in a principled and nonlinear learning framework. By combining the output of feature maps via a series of nonlinear classifiers, the new model consistently predicts eye movements better than any of its competitors. Saliency detection has been a hot topic in recent years. Its popularity is mainly because of its theoretical meaning for explaining human attention and applicable aims in segmentation, recognition, etc. Nevertheless, traditional algorithms are mostly based on unsupervised techniques, which have limited learning ability. The obtained saliency map is also inconsistent with many properties of human behavior. In order to overcome the challenges of inability and inconsistency, this paper presents a framework based on multiple-instance learning. Low-, mid-, and high-level features are incorporated in the detection procedure, and the learning ability enables it robust to noise. Experiments on a data set containing 1000 images demonstrate the effectiveness of the proposed framework. Its applicability is shown in the context of a seam carving application. The human visual system is foveated, that is, outside the central visual ﬁeld resolution and acuity drop rapidly.Nonetheless much of a visual scene is perceived after only a few saccadic eye movements, suggesting an effectivestrategy for selecting saccade targets. It has been known for some time that local image structure at saccade targetsinﬂuences the selection process. However, the question of what the most relevant visual features are is still under debate.Here we show that center-surround patterns emerge as the optimal solution for predicting saccade targets from their localimage structure. The resulting model, a one-layer feed-forward network, is surprisingly simple compared to previouslysuggested models which assume much more complex computations such as multi-scale processing and multiple featurechannels. Nevertheless, our model is equally predictive. Furthermore, our ﬁndings are consistent with neurophysiologicalhardware in the superior colliculus. Bottom-up visual saliency may thus not be computed cortically as has been thoughtpreviously.Keywords: visual saliency, eye movements, receptive ﬁeld analysis, classiﬁcation images, kernel methods,support vector machines, natural scenesCitation: Kienzle, W., Franz, M. O., Scholkopf, B., & Wichmann, F. A. (2009). Center-surround patterns emerge as optimalpredictors for human saccade targets. Journal of Vision, 9(5):7, 1–15, http: journalofvision.org 9 5 7 , doi:10.1167 9.5.7. Inspired by the primate visual system, computational saliency models decompose visual input into a set of feature maps across spatial scales in a number of pre-specified channels. The outputs of these feature maps are summed to yield the final saliency map. Here we use a least square technique to learn the weights associated with these maps from subjects freely fixating natural scenes drawn from four recent eye-tracking data sets. Depending on the data set, the weights can be quite different, with the face and orientation channels usually more important than color and intensity channels. Inter-subject differences are negligible. We also model a bias toward fixating at the center of images and consider both time-varying and constant factors that contribute to this bias. To compensate for the inadequacy of the standard method to judge performance (area under the ROC curve), we use two other metrics to comprehensively assess performance. Although our model retains the basic structure of the standard saliency model, it outperforms several state-of-the-art saliency algorithms. Furthermore, the simple structure makes the results applicable to numerous studies in psychophysics and physiology and leads to an extremely easy implementation for real-world applications.