Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble
Expert Systems with Applications: An International Journal
Integrating wavelets with clustering and indexing for effective content-based image retrieval
Knowledge-Based Systems
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This paper proposes WaveQ, a content-based image retrieval system that classifies images as texture or non-texture, then uses a Daubechies wavelet decomposition to extract feature vector information from the images, and finally applies the OPTICS clustering algorithm to cluster the extracted data into groups of similar images. Queries are submitted to WaveQ in the form of an example image. WaveQ has been thoroughly tested and the results are very promising. The achieved results demonstrate that the classification of images is extremely fast and accurate.