Progressive search and retrieval in large image archives
IBM Journal of Research and Development - Papers on mustimedia systems
An adaptive partitioning approach for mining discriminant regions in 3D image data
Journal of Intelligent Information Systems
The Many Facets of Progressive Retrieval for CBIR
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Discrete wavelet transform-based time series analysis and mining
ACM Computing Surveys (CSUR)
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We introduce a new framework for classifying large images (in the EOS; Earth Observing System) that is more accurate and less computationally expensive than the classical pixel-by-pixel approach. This approach, called progressive classification, is well suited for analyzing large images, such as multispectral satellite scenes, compressed with wavelet-based or block-transform-based transformations. These transformations produce a multiresolution pyramid representation of the data. A progressive classifier analyses the image at the coarsest resolution level, and it decides whether each coefficient corresponds to a homogeneous block of pixels in the original image or to a heterogeneous block. In the first case it labels the block, in the second case it recursively analyzes the region of the image at the immediately finer resolution level. Computational efficiency, compared to the classical approach, results from examining a much smaller number of coefficients than the number of pixels in the original image. Thus, progressive classification is a prime candidate as a content-based search operator for remotely-sensed data.