Image retrieval with feature selection and relevance feedback

This paper proposes a new content based image retrieval (CBIR) system combined with relevance feedback and the online feature selection procedures. A measure of inconsistency from relevance feedback is explicitly used as a new semantic criterion to guide the feature selection. By integrating the user feedback information, the feature selection is able to bridge the gap between low-level visual features and high-level semantic information, leading to the improved image retrieval accuracy. Experimental results show that the proposed method obtains higher retrieval accuracy than a commonly used approach.

[1]  Jing Peng,et al.  Region-based Image Retrieval Using Probabilistic Feature Relevance Learning , 2001, Pattern Analysis & Applications.

[2]  Mahmood R. Azimi-Sadjadi,et al.  An Adaptable Image Retrieval System With Relevance Feedback Using Kernel Machines and Selective Sampling , 2009, IEEE Transactions on Image Processing.

[3]  Tatik Maftukhah,et al.  Fuzzy Relevance Feedback in Image Retrieval for Color Feature Using Query Vector Modification Method , 2010, J. Adv. Comput. Intell. Intell. Informatics.

[4]  Alioune Ngom,et al.  Sequential Forward Selection Approach to the Non-unique Oligonucleotide Probe Selection Problem , 2008, PRIB.

[5]  Hui Zhang,et al.  Localized Content-Based Image Retrieval , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Constantine Kotropoulos,et al.  Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition , 2008, Signal Process..

[7]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[8]  Selim Aksoy,et al.  Automated feature selection through relevance feedback , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).