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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)
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training. This severely limits their scalability in realworld applications. To overcome this limitation, we develop a novel cross-dataset transfer learning approach to learn a discriminative(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, 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)
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)
In the area of image retrieval, post-retrieval processing is often used to refine the retrieval results to better satisfy users' requirements. Previous methods mainly focus on presenting users with relevant results. However, in most cases, users cannot clearly present their requirements by several query words. Therefore, relevant results with rich topic(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)