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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 group to get a(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)
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)
In this paper, we present our solutions for the WikipediaMM task at ImageCLEF 2008. The aim of this task is to investigate effective retrieval approaches in the context of a large-scale and heterogeneous collection of Wikipedia images that are searched by textual queries (and/or sample images and/or concepts) describing a user's information need. We first(More)
In this paper, we propose a novel linear discriminant analysis algorithm, referred to as LDA-FP, to train on-chip classifiers that can be implemented with low-power fixed-point arithmetic with extremely small word length. LDA-FP incorporates the non-idealities (i.e., rounding and overflow) associated with fixed-point arithmetic into the training process so(More)