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My dissertation work has involved in proposing a higher-level visual representation that enhances the traditional part-based bag of visual words (BOW) representation in many aspects. Firstly, we introduce a new multilayer semantic significance analysis (MSSA) model to select semantically significant visual words (SSVWs) from the classical visual words in order to overcome the noisiness of the feature quantization process (in collaboration with Prof. Zhongfei (Mark) Zhang, State University of New York at Binghamton). Secondly, we represent the spatial and the color constitution of an image as a mixture of n Gaussians in the feature space in order to propose a new spatial weighting scheme consists in weighting SSVWs according to the probability of each SSVW belongs to each of the n Gaussians. Thirdly, we strengthen the discrimination power of SSVWs by constructing semantically significant visual phrases (SSVPs) from frequently co-occur SSVWs in the same local context and semantically coherent. Finally, we introduce a new multiclass vote-based classifier (MVBC) based on this new higher-level visual representation. The large-scale extensive experimental results show that the proposed higher-level visual representation, Semantically Significant Invariant Visual Glossary (SSVIG), outperforms the traditional part-based image representation in retrieval, classification, object recognition, visual ranking, and visual summarization.
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