2010/12/23 by Nadia Baaziz, Baaziz, Nadia, Omar Abahmane +3
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Multimedia (cs.MM) #cs.CV #cs.IR #cs.MM
paper · pdf · doi:10.48550/arxiv.1012.5208
19 pages, 11 figures, 2 tables
arxiv created 2010/12/23 · openalex publication_date 2010/12/23 · arxiv updated 2010/12/24 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.