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To bridge the semantic gap between low-level visual features and high-level semantic concepts, this paper puts forward a novel feedback mechanism which is based on both instance and keyword features. In offline part, keyword space model is first constructed and updated using manifold ranking annotation; in online
models for categories specified simply by their names. We show that multiple-instance learning enables the recovery of robust category models from images returned by keyword-based search engines. By incorporating constraints that reflect the expected sparsity of true positive examples into a large-margin objective function
In this paper, we propose a novel multi-label image annotation for image retrieval based on annotated keywords. For multi-label image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image
Semantic soccer video analysis has attracted more and more attention recently. In this paper, we present a football event detection method by using multiple feature extraction and fusion. Instead of using low-level features, the proposed method is built upon visual, auditory features, text and audio keywords
Content-based image retrieval (CBIR) systems experience the challenge of semantic gap between the low-level visual features and the high-level semantic concepts. It would be advantageous to build CBIR systems which support high-level semantic query. The main idea is to integrate the strengths of content- and keyword
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