A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nesting and defoliation of index expressions for information retrieval
Knowledge and Information Systems
Cell histograms versus color histograms for image representation and retrieval
Knowledge and Information Systems
WALRUS: A Similarity Retrieval Algorithm for Image Databases
IEEE Transactions on Knowledge and Data Engineering
Design of a two-stage content-based image retrieval system using texture similarity
Information Processing and Management: an International Journal
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Knowledge and Information Systems
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IEEE Intelligent Systems
Query-sensitive similarity measures for information retrieval
Knowledge and Information Systems
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Knowledge and Information Systems
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Knowledge and Information Systems
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In this paper, an image retrieval method based on wavelet features is proposed. Due to the superiority in multiresolution analysis and spatial-frequency localization, the discrete wavelet transform (DWT) is used to extract wavelet features (i.e., approximations, horizontal details, vertical details, and diagonal details) at each resolution level. During the feature-extraction process, each image is first transformed from the standard RGB color space to the YUV space for the purpose of efficiency and ease of extracting the features based on color tones; then each component (i.e., Y, U, and V) of the image is further transformed to the wavelet domain. In the image database establishing phase, the wavelet coefficients of each image are stored; in the image retrieving phase, the system compares the wavelet coefficients of the Y, U, and V components of the query image with those of the images in the database, based on the weight factors adjusted by users, and find out good matches. To benefit from the user–machine interaction, a friendly graphic user interface (GUI) for fuzzy cognition is developed, allowing users to easily adjust weights for each feature according to their preferences. In our experiment, 1000 test images are used to demonstrate the effectiveness of our system.