Columbia University Department of Electrical Engineering and
Center for Telecommunications Research
New York, N.Y. 10027

Automated Binary Texture Feature Sets for Image Retrieval

John R. Smith and Shih-Fu Chang

Abstract:

Digital image and video libraries require new algorithms for the automated extraction and indexing of salient image features. Texture features provide one important cue for the visual perception and discrimination of image content. In this paper we propose a new approach for automated content extraction that allows for efficient database searching using texture features. The algorithm automatically extracts texture regions from image spatial-frequency data which are represented by binary texture feature vectors. We demonstrate that the binary texture features provide excellent performance in image query response time while providing highly effective texture discriminability, accuracy in spatial localization and capability for extraction from compressed data representations. We present the binary texture feature extraction and indexing technique and examine searching by texture on a database of 500 images.

• 1. Introduction
• 2. Texture Features
  • 2.1. Gabor functions
  • 2.2. Wavelet subband features
• 3. Automated Texture Analysis
  • 3.1. Texture feature set notation
  • 3.2. Texture feature extraction
  • 3.3. Texture retrieval
• 4. Summary
• References
• About this document ...