Interactive relevance visual learning for image retrieval

Hsin Chia Fu*, Z. H. Wang, W. J. Wang, Hsiao-Tien Pao

*Corresponding author for this work

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

Abstract

This paper proposes mixture Gaussian neural networks (MGNN) to learn visual features from user specified query image objects or regions for relevance image retrieval. Instead of segmenting query image regions from sample images, relevance feedback feature learning is performed by the proposed MGNN to extract query visual features. After feature learning, the MGNN can be used to measure the appearance difference between the query features and images for image retrieval. The proposed methods were tested on COREL image gallery and the WWW image collections, and testing results were compared with currently leading approaches. From the experimental results, that the extracted and learned query visual features by MGNN can be very close to users’ mind and/or desire, and the closeness is somewhat related to the number of feature leaning iterations. Since any dimensional data can be approximated by mixture Gaussian distributions, thus using MGNN to query and to retrieve similar and/or relevance high dimensional data or images will be a new area of research for future works.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence - 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Proceedings
EditorsIgnacio Rojas, Gonzalo Joya, Andreu Catala
PublisherSpringer Verlag
Pages227-240
Number of pages14
ISBN (Electronic)9783319192574
DOIs
StatePublished - 1 Jan 2015
Event13th International Work-Conference on Artificial Neural Networks, IWANN 2015 - Palma de Mallorca, Spain
Duration: 10 Jun 201512 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9094
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Work-Conference on Artificial Neural Networks, IWANN 2015
CountrySpain
CityPalma de Mallorca
Period10/06/1512/06/15

Keywords

  • Content-based image retrieval
  • Decision-based neural network
  • Mixture gaussian distribution
  • Reinforced and anti-reinforced learning
  • Visual

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  • Cite this

    Fu, H. C., Wang, Z. H., Wang, W. J., & Pao, H-T. (2015). Interactive relevance visual learning for image retrieval. In I. Rojas, G. Joya, & A. Catala (Eds.), Advances in Computational Intelligence - 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Proceedings (pp. 227-240). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9094). Springer Verlag. https://doi.org/10.1007/978-3-319-19258-1_20