Extraction of feature subspaces for content-based retrieval using relevance feedback

ABSTRACT

In the past few years, relevance feedback (RF) has been used as an effective solution for content-based image retrieval (CBIR). Although effective, the RF-CBIR framework does not address the issue of feature extraction for dimension reduction and noise reduction. In this paper, we propose a novel method for extracting features for the class of images represented by the positive images provided by subjective RF. Principal Component Analysis (PCA) is used to reduce both noise contained in the original image features and dimensionality of feature spaces. The method increases the retrieval speed and reduces the memory significantly without sacrificing the retrieval accuracy.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Relevance feedback

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Indexing methods
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

General Terms:
Algorithms, Design, Performance

Keywords:
Bayesian estimation, content-based image retrieval (CBIR), principal component analysis (PCA), relevance feedback