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- Title
Click data guided query modeling with click propagation and sparse coding.
- Authors
Tan, Min; Yu, Jun; Huang, Qingming; Wu, Weichen
- Abstract
We address the problem of fine-grained image recognition 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, making the click feature be high-dimensional and sparse. We propose a novel query modeling approach to merge semantically similar queries, and construct a compact click feature with the merged queries. To deal with the sparsity and in-consistency in click feature, we design a graph based propagation approach to predict the zero-clicks, ensuring similar images have similar clicks for each query. Afterwards, using the propagated click feature, we formulate the query merging problem as a sparse coding based recognition task. In addition, the hot queries are utilized to construct the dictionary. 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. In the query merging procedure, sparse coding performs better than traditional K-mean algorithm. Also, the “hot queries” outperform K-SVD in dictionary learning.
- Subjects
MULTIMEDIA systems; SEARCH engines; IMAGE recognition (Computer vision); COMPUTER programming; RESEARCH equipment; EQUIPMENT &; supplies
- Publication
Multimedia Tools & Applications, 2018, Vol 77, Issue 17, p22145
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-018-5703-4