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Recent years have witnessed the great success of social media websites. Tag-based image search is an important approach to accessing the image content on these websites. However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or not diverse. This paper proposes a diverse relevance ranking scheme that is able to take relevance and diversity into...
Conventional approaches to automatic image annotation usually suffer from two problems: (1) They cannot guarantee a good semantic coherence of the annotated words for each image, as they treat each word independently without considering the inherent semantic coherence among the words; (2) They heavily rely on visual similarity for judging semantic similarity. To address the above issues, we propose...
We propose a new multiple instance learning (MIL) algorithm to learn image categories. Unlike existing MIL algorithms, in which the individual instances in a bag are assumed to be independent with each other, we develop concurrent tensors to explicitly model the inter-dependency between the instances to better capture image's inherent semantics. Rank-1 tensor factorization is then applied to obtain...
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