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Although much progress has been made, current low-level based visual information retrieval technology does not allow users to formulate queries through high-level semantics. More and more digitized art images appear on the Internet, and techniques need to be established on how to organize and retrieve them. In this work, a framework for retrieving art(More)
Smart phones is bringing about emerging potentials in mobile visual search. Extensive research efforts have been made in compact visual descriptors. However, directly extracting visual descriptors on a mobile device is computationally intensive and time consuming. Towards low bit rate visual search, we propose to deeply compress query images by learning a(More)
Extraction and transmission of compact descriptors are of great importance for next-generation mobile visual search applications. Existing visual descriptor techniques mainly compress visual features into compact codes of fixed bit rate, which is not adaptive to the bandwidth fluctuation in wireless environment. In this letter, we propose a Rate-adaptive(More)
In this paper, we present a probabilistic multi-task learning approach for visual saliency estimation in video. In our approach, the problem of visual saliency estimation is modeled by simultaneously considering the stimulus-driven and task-related factors in a probabilistic framework. In this framework, a stimulus-driven component simulates the low-level(More)
For the real-time and low-delay video surveillance and teleconferencing applications, the newly video coding standard HEVC can achieve much higher coding efficiency over H.264/AVC. However, we still argue that the hierarchical prediction structure in the HEVC low-delay encoder still does not fully utilize the special characteristics of surveillance and(More)
This letter presents a novel approach for visual saliency estimation through single image optimization. Instead of directly mapping visual features to saliency values with a unified model, we treat regional saliency values as the optimization objective on each single image. By using a quadratic programming framework, our approach can adaptively optimize the(More)
In this paper, a group-sensitive multiple kernel learning (GS-MKL) method is proposed for object recognition to accommodate the intraclass diversity and the interclass correlation. By introducing the “group” between the object category and individual images as an intermediate representation, GS-MKL attempts to learn group-sensitive multikernel(More)
Visual saliency is a useful cue to locate the conspicuous image content. To estimate saliency, many approaches have been proposed to detect the unique or rare visual stimuli. However, such bottom-up solutions are often insufficient since the prior knowledge, which often indicates a biased selectivity on the input stimuli, is not taken into account. To solve(More)
Object segmentation is widely recognized as one of the most challenging problems in computer vision. One major problem of existing methods is that most of them are vulnerable to the cluttered background. Moreover, human intervention is often required to specify foreground/background priors, which restricts the usage of object segmentation in real-world(More)