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In this paper, we propose a group-sensitive multiple kernel learning (GS-MKL) method to accommodate the intra-class diversity and the inter-class correlation for object categorization. By introducing an intermediate representation “group” between images and object categories, GS-MKL attempts to find appropriate kernel combination for each(More)
Deep-sea tubeworms in the annelid family Siboglinidae have drawn considerable interest regarding their ecology and evolutionary biology. As adults, they lack a digestive tract and rely on endosymbionts for nutrition. Moreover, they are important members of chemosynthetic environments including hydrothermal vents, cold seeps, muddy sediments, and whale(More)
Humans' ability to rapidly and accurately detect, identify and classify faces under variable conditions derives from a network of brain regions highly tuned to face information. The fusiform face area (FFA) is thought to be a computational hub for face processing; however, temporal dynamics of face information processing in FFA remains unclear. Here we use(More)
Recently, multiple kernel learning (MKL) methods have shown promising performance in image classification. As a sort of supervised learning, training MKL-based classifiers relies on selecting and annotating extensive dataset. In general, we have to manually label large amount of samples to achieve desirable MKL-based classifiers. Moreover, MKL also suffers(More)
The nature of the visual representation for words has been fiercely debated for over 150 y. We used direct brain stimulation, pre- and postsurgical behavioral measures, and intracranial electroencephalography to provide support for, and elaborate upon, the visual word form hypothesis. This hypothesis states that activity in the left midfusiform gyrus (lmFG)(More)
Mobile devices are increasingly powerful in media storage and rendering. The prevalent request of decent video browsing on mobile devices is demanding. However, one limitation comes from the size and aspect constraints of display. To display a video on a small screen, rendering process probably undergoes a sort of retargeting to fit into the target display(More)
In this paper, we present a robust and unsupervised approach for recognition of object categories, RTSI-pLSA, which overcomes the weakness of TSI-pLSA in recognizing rotated objects in images. Our approach uses radial template to describe spatial information (position, scale and orientation) of an object. A bottom up heuristical and unsupervised scheme is(More)
Content-based copy detection (CBCD) over large corpus with complex transformations is important but challenging for video content analysis. To accomplish the TRECVid 2010 CBCD task, we’ve proposed a copy detection approach which exploits complementary visual/audio features and sequential pyramid matching (SPM). Several independent detectors first match(More)