OCRS: An interactive object-based image clustering and retrieval system

Academic Article

Abstract

  • In this paper, we propose an Interactive Object-based Image Clustering and Retrieval System (OCRS). The system incorporates two major modules: Preprocessing and Object-based Image Retrieval. In preprocessing, an unsupervised segmentation method called WavSeg is used to segment images into meaningful semantic regions (image objects). This is an area where a huge number of image regions are involved. Therefore, we propose a Genetic Algorithm based algorithm to cluster these images objects and thus reduce the search space for object-based image retrieval. In the learning and retrieval module, the Diverse Density algorithm is adopted to analyze the user's interest and generate the initial hypothesis which provides a prototype for future learning and retrieval. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. In interacting with user, we propose to use One-Class Support Vector Machine (SVM) to learn the user's interest and refine the returned result. Performance is evaluated on a large image database and the effectiveness of our retrieval algorithm is demonstrated through comparative studies. © 2007 Springer Science+Business Media, LLC.
  • Authors

    Published In

  • Multimedia Systems  Journal
  • Digital Object Identifier (doi)

    Author List

  • Zhang C; Chen X
  • Start Page

  • 71
  • End Page

  • 89
  • Volume

  • 35
  • Issue

  • 1