Fundamentals of digital image processing
Fundamentals of digital image processing
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Multiple evidence combination in image retrieval: Diogenes searches for people on the Web
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Building efficient and effective metasearch engines
ACM Computing Surveys (CSUR)
Visually Searching the Web for Content
IEEE MultiMedia
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
WebSeer: An Image Search Engine for the World Wide Web
WebSeer: An Image Search Engine for the World Wide Web
Automated binary texture feature sets for image retrieval
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
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Advances in content-based image retrieval(CBIR)lead to numerous efficient techniques for retrieving images based on their content features, such as colours, textures and shapes. However, CBIR to date has been mainly focused on a centralised environment, ignoring the rapidly increasing image collection in the world, the images on the Web. In this paper, we study the problem of distributed CBIR in the environment of the Web where image collections are represented as normal and typically autonomous websites. After an analysis of challenging issues in applying current CBIR techniques to this new environment, we explore architectural possibilities and discuss their advantages and disadvantages. Finally we present a case study of distributed CBIR based exclusively on texture features. A new method to derive texture-based global similarity ranking suggests that, with a deep understanding of feature extraction algorithms, it is possible to have a better and more predictable way to merge local rankings from heterogeneous sources than using the commonly used method of assigning different weights.