A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Biologically Inspired Saliency Map Model for Bottom-up Visual Attention
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Gaze-based interaction for semi-automatic photo cropping
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A Coherent Computational Approach to Model Bottom-Up Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
2006 Special Issue: Modeling attention to salient proto-objects
Neural Networks
Salient region detection by modeling distributions of color and orientation
IEEE Transactions on Multimedia
Interactive image segmentation using probabilistic hypergraphs
Pattern Recognition
Goal-directed search with a top-down modulated computational attention system
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Stochastic bottom-up fixation prediction and saccade generation
Image and Vision Computing
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We formulate the problem of salient object detection in images as an automatic labeling problem on the vertices of a weighted graph. The seed (labeled) nodes are first detected using Markov random walks performed on two different graphs that represent the image. While the global properties of the image are computed from the random walk on a complete graph, the local properties are computed from a sparse k-regular graph. The most salient node is selected as the one which is globally most isolated but falls on a locally compact object. A few background nodes and salient nodes are further identified based upon the random walk based hitting time to the most salient node. The salient nodes and the background nodes will constitute the labeled nodes. A new graph representation of the image that represents the saliency between nodes more accurately, the "pop-out graph" model, is computed further based upon the knowledge of the labeled salient and background nodes. A semisupervised learning technique is used to determine the labels of the unlabeled nodes by optimizing a smoothness objective label function on the newly created "pop-out graph" model along with some weighted soft constraints on the labeled nodes.