Semantic manifold learning for image retrieval

Yen Yu Lin, Tyng Luh Liu, Hwann Tzong Chen

Research output: Chapter in Book/Report/Conference proceedingChapter

79 Scopus citations


Learning the user's semantics for CBIR involves two different sources of information: the similarity relations entailed by the content-based features, and the relevance relations specified in the feedback. Given that, we propose an augmented relation embedding (ARE) to map the image space into a semantic manifold that faithfully grasps the user's preferences. Besides ARE, we also look into the issues of selecting a good feature set for improving the retrieval performance. With these two aspects of efforts we have established a system that yields far better results than those previously reported. Overall, our approach can be characterized by three key properties: 1) The framework uses one relational graph to describe the similarity relations, and the other two to encode the relevant/irrelevant relations indicated in the feedback. 2) With the relational graphs so defined, learning a semantic manifold can be transformed into solving a constrained optimization problem, and is reduced to the ARE algorithm accounting for both the representation and the classification points of views. 3) An image representation based on augmented features is introduced to couple with the ARE learning. The use of these features is significant in capturing the semantics concerning different scales of image regions. We conclude with experimental results and comparisons to demonstrate the effectiveness of our method.
Original languageAmerican English
Title of host publicationProceedings of the 13th ACM International Conference on Multimedia, MM 2005
Number of pages10
StatePublished - 2005

Publication series

NameProceedings of the 13th ACM International Conference on Multimedia, MM 2005


  • Dimensionality Reduction
  • Feature Selection
  • Image Retrieval
  • Manifold Learning
  • Relevance Feedback

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    Lin, Y. Y., Liu, T. L., & Chen, H. T. (2005). Semantic manifold learning for image retrieval. In Proceedings of the 13th ACM International Conference on Multimedia, MM 2005 (pp. 249-258). (Proceedings of the 13th ACM International Conference on Multimedia, MM 2005).