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ICIAR
2007
Springer

Image Retrieval Using Transaction-Based and SVM-Based Learning in Relevance Feedback Sessions

9 years 2 months ago
Image Retrieval Using Transaction-Based and SVM-Based Learning in Relevance Feedback Sessions
This paper introduces a composite relevance feedback approach for image retrieval using transaction-based and SVM-based learning. A transaction repository is dynamically constructed by applying these two learning techniques on positive and negative session-term feedback. This repository semantically relates each database image to the query images having been used to date. The query semantic feature vector can then be computed using the current feedback and the semantic values in the repository. The correlation measures the semantic similarity between the query image and each database image. Furthermore, the SVM is applied on the session-term feedback to learn the hyperplane for measuring the visual similarity between the query image and each database image. These two similarity measures are normalized and combined to return the retrieved images. Our extensive experimental results show that the proposed approach offers average retrieval precision as high as 88.59% after three iterations...
Xiaojun Qi, Ran Chang
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ICIAR
Authors Xiaojun Qi, Ran Chang
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