Content representation and similarity matching for texture-based image retrieval

ABSTRACT

This paper addresses the fundamental issues of visual content representation and similarity matching in content-based image retrieval and image databases in general. Simply stated, defining an image retrieval system is equivalent to find answers to two fundamental questions: 1. Representation model or which features are used to represent the content of images; 2. Once the set of features representing the content of images is determined, the question of how to combine the individual or partial similarities according to each feature to form a global similarity must be addressed. In this paper, a new similarity model is introduced based on the Gower coefficient of similarity. This similarity model is flexible and can be declined in several versions: non-weighted, weighted and hierarchical versions. This model was applied to a sample of homogeneous textured images considering two representation models: the autoregressive model, a purely statistical model, and an empirical perceptual model based on perceptual features such as coarseness and directionality. Experimentations show very interesting results.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.7 Feature Measurement

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Image databases
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process; Retrieval models

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Similarity measures

General Terms:
Design, Experimentation, Human Factors, Measurement

Keywords:
autoregressive model, content-based image retrieval, perceptual evaluation, perceptual model