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In this paper, we present a unified statistical framework for modeling both saccadic eye movements and visual saliency. By analyzing the statistical properties of human eye fixations on natural images, we found that human attention is sparsely distributed and usually deployed to locations with abundant structural information. This new observations inspired us to model saccadic behavior and visual...
Attribute-based query offers an intuitive way of image retrieval, in which users can describe the intended search targets with understandable attributes. In this paper, we develop a general and powerful framework to solve this problem by leveraging a large pool of weak attributes comprised of automatic classifier scores or other mid-level representations that can be easily acquired with little or...
We introduce a Compact Topical Descriptor to learn a compact yet discriminative image signature from the reference image corpus. This descriptor is deployed over the well used bag-of-words image histogram, with two merits over the traditional topical features: First, we propose to directly control the topical sparsity to achieve the descriptor compactness. Second, we ensure the descriptor discriminability...
Visual vocabulary serves as a fundamental component in many computer vision tasks, such as object recognition, visual search, and scene modeling. While state-of-the-art approaches build visual vocabulary based solely on visual statistics of local image patches, the correlative image labels are left unexploited in generating visual words. In this work, we present a semantic embedding framework to integrate...
Mining actor correlations from TV series enables semantic level video understanding and facilitates users to conduct correlation-based query. In this paper, we introduce a graph-based actor correlations mining framework, which serves as the first attempt for effective actor association presentation and concurrence search. We leverage face detection and tracking to locate actors with 2D-PCA detector...
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