Extraction of major object features using VQ clustering for content-based image retrieval

Hun Woo Yoo, She Hwan Jung, Dong Sik Jang, Yoon Kyoon Na

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)


An image representation method using vector quantization (VQ) on color and texture is proposed in this paper. The proposed method is also used to retrieve similar images from database systems. The basic idea is a transformation from the raw pixel data to a small set of image regions, which are coherent in color and texture space. A scheme is provided for object-based image retrieval. Features for image retrieval are the three color features (hue, saturation, and value) from the HSV color model and five textural features (ASM, contrast, correlation, variance, and entropy) from the gray-level co-occurrence matrices. Once the features are extracted from an image, eight-dimensional feature vectors represent each pixel in the image. The VQ algorithm is used to rapidly cluster those feature vectors into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to the object within the image. This method can retrieve similar images even in cases where objects are translated, scaled, and rotated.

Original languageEnglish
Pages (from-to)1115-1126
Number of pages12
JournalPattern Recognition
Issue number5
Publication statusPublished - 2002 May


  • Content-based image retrieval
  • Object-based image retrieval
  • Vector quantization (VQ) clustering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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