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Personalized Visual Vocabulary Adaption for Social Image Retrieval

With the popularity of mobile devices and social networks, users can easily build their personalized image sets. Thus, personalized image analysis, indexing, and retrieval have become important topics in social media analysis. Because of users' diverse preferences, their personalized image sets are usually related to specific topics and show large feature distribution bias from general Internet images. Therefore, the visual vocabulary trained on general Internet images may could not fit across users' personalized image sets very well. To improve the image retrieval performance on personalized image sets, we propose the personalized visual vocabulary adaption which removes non-discriminative visual words and replaces them with more exact and discriminative ones, i.e., adapt a general vocabulary toward a specific user's image set. The proposed algorithm updates the visual vocabulary during off-line feature quantization, and operates on a limited number of visual words, hence shows satisfying efficiency. Extensive experiments of image search on public datasets demonstrate the efficiency and superior performance of our approach.