Learning a weighted semantic manifold for content-based image retrieval

Ran Chang, Zhongmiao Xiao, Koksheik Wong, Xiaojun Qi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


We propose a novel weighted semantic manifold ranking system for content-based image retrieval. This manifold builds a more accurate intrinsic structure for the proper image space by combining visual and semantic relevance relations. Specifically, we apply the learning mechanism to capture users' semantic concepts in clusters and extract high-level semantic features for each database image. We then incorporate the reliability score, the fuzzy membership, and the composite low-level and high-level relation into the traditional affinity matrix to construct a weighted semantic manifold structure. We finally create an asymmetric relevance vector to propagate positive and negative labels via the proposed manifold structure to images with high similarities. Extensive experiments demonstrate our system outperforms other manifold systems and learning systems in the context of both correct and erroneous feedback.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
Number of pages4
Publication statusPublished - 2012
Externally publishedYes
EventIEEE International Conference on Image Processing 2012 - Coronado Springs - Disney World, Orlando, United States of America
Duration: 30 Sept 20123 Oct 2012
Conference number: 19th
https://ieeexplore.ieee.org/xpl/conhome/6451323/proceeding (Proceedings)

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


ConferenceIEEE International Conference on Image Processing 2012
Abbreviated titleICIP 2012
Country/TerritoryUnited States of America
Internet address


  • CBIR
  • semantic clusters
  • semantic features
  • weighted semantic manifold

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