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Samuel Barrett, Ran Chang, and Xiaojun Qi. A fuzzy combined learning approach to content-based image retrieval. In IEEE International Conference on Multimedia and Expo (ICME), pp. 838 –841, July 2009.
We propose a fuzzy combined learning approach to construct a relevance feedback-based content-based image retrieval (CBIR) system for efficient image search. Our system uses a composite short-term and long-term learning approach to learn the semantics of an image. Specifically, the short-term learning technique applies fuzzy support vector machine (FSVM) learning on user labeled and additional chosen image blocks to learn a more accurate boundary for separating the relevant and irrelevant blocks at each feedback iteration. The long-term learning technique applies a novel semantic clustering technique to adaptively learn and update the semantic concepts at each query session. A predictive algorithm is also applied to find images most semantically related to the query based on the semantic clusters generated in the long-term learning. Our extensive experimental results demonstrate the proposed system outperforms several state-of-the-art peer systems in terms of both retrieval precision and storage space.
@INPROCEEDINGS{ICME09-Barrett,
author={Samuel Barrett and Ran Chang and Xiaojun Qi},
booktitle={IEEE International Conference on Multimedia and Expo (ICME)},
title={A fuzzy combined learning approach to content-based image retrieval},
year={2009},
month={July},
pages={838 -841},
keywords={adaptive learning;content-based image retrieval;feedback iteration;fuzzy combined learning approach;fuzzy support vector machine;image search;long-term learning approach;query session;relevance feedback-based CBIR;semantic cluster generation;semantic clustering technique;short-term learning approach;content-based retrieval;fuzzy logic;image retrieval;iterative methods;learning (artificial intelligence);pattern clustering;relevance feedback;support vector machines;},
doi={10.1109/ICME.2009.5202625},
ISSN={1945-7871},
abstract={
We propose a fuzzy combined learning approach to construct a relevance
feedback-based content-based image retrieval (CBIR) system for efficient
image search. Our system uses a composite short-term and long-term learning
approach to learn the semantics of an image. Specifically, the short-term
learning technique applies fuzzy support vector machine (FSVM) learning on
user labeled and additional chosen image blocks to learn a more accurate
boundary for separating the relevant and irrelevant blocks at each feedback
iteration. The long-term learning technique applies a novel semantic
clustering technique to adaptively learn and update the semantic concepts at
each query session. A predictive algorithm is also applied to find images
most semantically related to the query based on the semantic clusters
generated in the long-term learning. Our extensive experimental results
demonstrate the proposed system outperforms several state-of-the-art peer
systems in terms of both retrieval precision and storage space.
}
}
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