An Adaptable Image Retrieval System With Relevance Feedback Using Kernel Machines and Selective Sampling
- 12 May 2009
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 18 (7) , 1645-1659
- https://doi.org/10.1109/tip.2009.2017825
Abstract
This paper presents an adaptable content-based image retrieval (CBIR) system developed using regularization theory, kernel-based machines, and Fisher information measure. The system consists of a retrieval subsystem that carries out similarity matching using image-dependant information, multiple mapping subsystems that adaptively modify the similarity measures, and a relevance feedback mechanism that incorporates user information. The adaptation process drives the retrieval error to zero in order to exactly meet either an existing multiclass classification model or the user high-level concepts using reference-model or relevance feedback learning, respectively. To facilitate the selection of the most informative query images during relevance feedback learning a new method based upon the Fisher information is introduced. Model-reference and relevance feedback learning mechanisms are thoroughly tested on a domain-specific image database that encompasses a wide range of underwater objects captured using an electro-optical sensor. Benchmarking results with two other relevance feedback learning methods are also provided.Keywords
This publication has 25 references indexed in Scilit:
- Which Components are Important for Interactive Image Searching?IEEE Transactions on Circuits and Systems for Video Technology, 2008
- Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image RetrievalIEEE Transactions on Image Processing, 2007
- Negative Samples Analysis in Relevance FeedbackIEEE Transactions on Knowledge and Data Engineering, 2007
- Multitraining Support Vector Machine for Image RetrievalIEEE Transactions on Image Processing, 2006
- Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithmIEEE Transactions on Multimedia, 2006
- Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrievalIEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
- An Interactive Approach for CBIR Using a Network of Radial Basis FunctionsIEEE Transactions on Multimedia, 2004
- A Bayesian framework for content-based indexing and retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Modeling score distributions for combining the outputs of search enginesPublished by Association for Computing Machinery (ACM) ,2001
- Textural Features for Image ClassificationIEEE Transactions on Systems, Man, and Cybernetics, 1973