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Easy photo-taking and photo-sharing today make image an increasingly important type of media in people's everyday life, which arouses a growing demand for a practical image understanding technique. Traditional computer vision or machine learning methods which learn models based on a set of training data are still in the stage of tackling hundreds of object categories. Such a scale is far from practical...
Scalable image retrieval systems usually involve hierarchical quantization of local image descriptors, which produces a visual vocabulary for inverted indexing of images. Although hierarchical quantization has the merit of retrieval efficiency, the resulting visual vocabulary representation usually faces two crucial problems: (1) hierarchical quantization errors and biases in the generation of ldquovisual...
Recently, there has been growing interest in mining co-location visual patterns from a collection of images. To find a proper usage of visual patterns in near-duplicate image retrieval systems, we study a TF-IDF weighting function for visual patterns. We show usage of TF and IDF respectively in this weighting function. Experiments demonstrate that 1) visual patterns and words should be weighted separately;...
Conventional approaches to video annotation predominantly focus on supervised identification of a limited set of concepts, while unsupervised annotation with infinite vocabulary remains unexplored. This work aims to exploit the overlap in content of news video to automatically annotate by mining similar videos that reinforce, filter, and improve the original annotations. The algorithm employs a two-step...
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