A number of psychological and physiological evidences suggest that early visual attention works in a coarse-to-fine way, which lays a basis for the reverse hierarchy theory (RHT). This theory states that attention propagates from the top level of the visual hierarchy that processes gist and abstract information of input, to the bottom level that processes local details. Inspired by the theory, we develop a computational model for saliency detection in images. First, the original image is downsampled to different scales to constitute a pyramid. Then, saliency on each layer is obtained by image super-resolution reconstruction from the layer above, which is defined as unpredictability from this coarse-to-fine reconstruction. Finally, saliency on each layer of the pyramid is fused into stochastic fixations through a probabilistic model, where attention initiates from the top layer and propagates downward through the pyramid. Extensive experiments on two standard eye-tracking datasets show that the proposed method can achieve competitive results with state-of-the-art models.
Different from our proposal, the conventional practice in fusing saliency at different image scales and feature channels was through linear combination. Borji @cite_10 proposed a model that combined a global saliency model AIM @cite_31 and a local model @cite_17 @cite_37 through linear addition of normalized maps. Some models learned the linear combination weights for feature channels. Judd al @cite_32 trained a linear SVM from human eye fixation data to optimally combine the activation of several low-, mid- and high-level features. With a similar idea, Zhao and Koch @cite_44 adopted a regression-based approach.
@cite_37 @cite_32 @cite_44 @cite_31 @cite_10 @cite_17
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In this paper, we present a novel model for saliency prediction under a unified framework of feature integration. The model distinguishes itself by directly learning from natural images and automatically incorporating higher-level semantic information in a scalable manner for gaze prediction. Unlike most existing saliency models that rely on specific features or object detectors, our model learns multiple stages of features that mimic the hierarchical organization of the ventral stream in the visual cortex and integrate them by adapting their weights based on the ground-truth fixation data. To accomplish this, we utilize a multi-layer sparse network to learn low-, mid- and high-level features from natural images and train a linear support vector machine (SVM) for weight adaption and feature integration. Experimental results show that our model could learn high-level semantic features like faces and texts and can perform competitively among existing approaches in predicting eye fixations. A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: first forming activation maps on certain feature channels, and then normalizing them in a way which highlights conspicuity and admits combination with other maps. The model is simple, and biologically plausible insofar as it is naturally parallelized. This model powerfully predicts human fixations on 749 variations of 108 natural images, achieving 98 of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch ([2], [3], [4]) achieve only 84 . 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. Researchers have been taking advantage of visual attention in various image processing applications such as image retargeting, video coding, etc. Recently, many saliency detection algorithms have been proposed by extracting features in spatial or transform domains. In this paper, a novel saliency detection model is introduced by utilizing low-level features obtained from the wavelet transform domain. Firstly, wavelet transform is employed to create the multi-scale feature maps which can represent different features from edge to texture. Then, we propose a computational model for the saliency map from these features. The proposed model aims to modulate local contrast at a location with its global saliency computed based on the likelihood of the features, and the proposed model considers local center-surround differences and global contrast in the final saliency map. Experimental evaluation depicts the promising results from the proposed model by outperforming the relevant state of the art saliency detection models.