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PSI: A probabilistic semantic interpretable framework for fine‐grained image ranking
Author(s) -
Li Hanhui,
Wu Hefeng,
Li Donghui,
Lin Shujin,
Su Zhuo,
Luo Xiaonan
Publication year - 2018
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.24068
Subject(s) - ranking (information retrieval) , computer science , benchmark (surveying) , machine learning , artificial intelligence , probabilistic logic , representation (politics) , image (mathematics) , focus (optics) , cluster analysis , semantic gap , learning to rank , key (lock) , probabilistic latent semantic analysis , ranking svm , class (philosophy) , information retrieval , pattern recognition (psychology) , data mining , image retrieval , physics , computer security , geodesy , optics , politics , political science , law , geography
Image Ranking is one of the key problems in information science research area. However, most current methods focus on increasing the performance, leaving the semantic gap problem, which refers to the learned ranking models are hard to be understood, remaining intact. Therefore, in this article, we aim at learning an interpretable ranking model to tackle the semantic gap in fine‐grained image ranking. We propose to combine attribute‐based representation and online passive‐aggressive (PA) learning based ranking models to achieve this goal. Besides, considering the highly localized instances in fine‐grained image ranking, we introduce a supervised constrained clustering method to gather class‐balanced training instances for local PA‐based models, and incorporate the learned local models into a unified probabilistic framework. Extensive experiments on the benchmark demonstrate that the proposed framework outperforms state‐of‐the‐art methods in terms of accuracy and speed.