A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
IRM: integrated region matching for image retrieval
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Montage: An Image Database for the Fashion, Textile, and Clothing Industry in Hong Kong
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Wavelet transform-based locally orderless images for texture segmentation
Pattern Recognition Letters
Multiresolution Wavelet Transform and Supervised Learning for Content-Based Image Retrieval
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
Nonwoven uniformity identification using wavelet texture analysis and LVQ neural network
Expert Systems with Applications: An International Journal
Image retrieval by local contrast patterns and color
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network
Journal of Medical Systems
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For the efficient and cost effective management of large volume of images in textile industry, an effective retrieval system is expected. Textile (e.g., curtain) images of raw clothes have wide varieties of design patterns. Despite many research works in this area, only a few emphasize on complex pattern characteristics. Such patterns are horizontal, vertical, cross-stripes, leaves and flowers in curtain database. In this study, we propose a system that retrieves images based on wavelet domain perceptual features which mainly depend on edge and correlation characteristics of the wavelet sub-bands in the major directions (horizontal and vertical). In order to reduce searching time, we first catagorize various patterns using supervised learning vector quantization (LVQ) technique. Then for each category or group, a prototype vector is formed by averaging all classified feature vectors in it. For a typical query, the query key is first compared with a few prototype vectors to determine the expected category. Then the query key performs similarity comparisons with the population of that particular group and retrieves relevant images. Users have also the provision to select subsequent similar groups if any query fails to capture the correct group at first attempt. An experiment with a set of curtain images shows the effectiveness of the proposed features compared to conventional Gabor, pyramidal wavelet transform (PWT) or local binary pattern (LBP) features. wavelet transform (PWT) or local binary pattern (LBP) features.