Texture feature extraction in the spatial-frequency domain for content-based image retrieval

TL;DR

This paper reviews advanced texture feature extraction methods in the spatial-frequency domain for content-based image retrieval, emphasizing techniques like wavelets and contourlets to improve accuracy and efficiency.

Contribution

It provides a comprehensive overview of current spatial-frequency texture extraction techniques and their effectiveness in improving image retrieval systems.

Findings

Wavelet and Gabor-based features enhance retrieval accuracy.

Contourlet transforms capture directional texture information.

Spatial-frequency methods balance accuracy and computational complexity.

Abstract

The advent of large scale multimedia databases has led to great challenges in content-based image retrieval (CBIR). Even though CBIR is considered an emerging field of research, however it constitutes a strong background for new methodologies and systems implementations. Therefore, many research contributions are focusing on techniques enabling higher image retrieval accuracy while preserving low level of computational complexity. Image retrieval based on texture features is receiving special attention because of the omnipresence of this visual feature in most real-world images. This paper highlights the state-of-the-art and current progress relevant to texture-based image retrieval and spatial-frequency image representations. In particular, it gives an overview of statistical methodologies and techniques employed for texture feature extraction using most popular spatial-frequency image transforms, namely discrete wavelets, Gabor wavelets, dual-tree complex wavelet and contourlets. Indications are also given about used similarity measurement functions and most important achieved results.