Multi-View Sampling for Relevance Feedback in Image Retrieval

  • Authors:

  • Jian Cheng;Kongqiao Wang

  • Affiliations:

  • Beijing University of Posts and Telecommunications, Beijing 100876, China;Nokia Research Center, No.11 He Ping Li Dong Jie, Nokia House 1, Beijing 100013,China

  • Venue:
  • ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Labelling is a boring task for users in relevance feedback. How to maximumly reduce the labelling is crucial for relevance feedback algorithms. In spirited by active learning and Co-Testing, we proposed a Co-SVM algorithm to improve the efficiency and effectiveness of selective sampling in image retrieval. In Co-SVM, color and texture are looked as sufficient and uncorrelated views of an image. SVM classifier is learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabelled data. These unlabelled samples that disagree in the two classifiers are chose to label. The experimental results show that the proposed algorithm is beneficial to image retrieval.