PISAR: Progressive image search and recommendation system by auto-interpretation and user behavior

Jen-Wei Huang, Chi Yao Tseng, Meng Cheng Chen, Ming Syan Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Many image search engines nowadays still struggle with the semantic gap between low level image features and high level image concepts. Some solutions are proposed to bridge the gap by using surrounding texts of images or by adding tags on images by single user. However, they can only provide obscure or limited information about images. Another problem is that users may not know exactly what they want when they search for images. In this work, we proposed a Progressive Image Search And Recommendation system, named as PISAR, to reduce the semantic gap by incorporating the auto-interpretation and user behavior. PISAR is able to progressively improve the interpretation of images and provide a list of recommendation. The evaluation results show that with the help of auto-interpretation and user behavior, the performance of search results and recommendation results can be progressively improved.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
Pages1442-1447
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 23
Event2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK, United States
Duration: 2011 Oct 92011 Oct 12

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Other

Other2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
CountryUnited States
CityAnchorage, AK
Period11-10-0911-10-12

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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