Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/60455
Title: Content-based image retrieval with relevance feedback using adaptive processing of tree-structure image representation
Authors: Wang, Z
Chi, Z 
Feng, D
Tsoi, AC
Keywords: Image representation
Content-based image retrieval
Relevance feedback
Adaptive processing of data structures
Back-propagation through structure
Neural networks
Issue Date: 2003
Publisher: World Scientific
Source: International journal of image and graphics, 2003, v. 3, no. 1, p. 119-143 How to cite?
Journal: International journal of image and graphics 
Abstract: Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.
URI: http://hdl.handle.net/10397/60455
ISSN: 0219-4678
EISSN: 1793-6756
DOI: 10.1142/S0219467803000944
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