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- W2790479940 abstract "We address the fine-grained image recognition problem using user click data, wherein each image is represented as a semantical query-click feature vector. Usually, the query set obtained from search engines is large-scale and redundant. We propose a novel query modeling approach to merge semantically similar queries and construct a compact click feature. We represent each query as a click feature, and design a graph based propagation approach to predict the zero-clicks, ensuring similar images have similar clicks. Afterwards, using this feature, we formulate the problem as a sparse coding based recognition task, wherein the dictionary is discriminatively trained. We evaluate our method for fine-grained image recognition on the public Clickture-Dog dataset. It is shown that, the propagated click feature performs much better than the original one. Also, sparse coding performs better than K-means in query merging." @default.
- W2790479940 created "2018-03-29" @default.
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- W2790479940 date "2018-01-01" @default.
- W2790479940 modified "2023-09-27" @default.
- W2790479940 title "Query Modeling for Click Data Based Image Recognition Using Graph Based Propagation and Sparse Coding" @default.
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- W2790479940 doi "https://doi.org/10.1007/978-981-10-8530-7_19" @default.
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