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We address the problem of retrieving the silhouettes of objects from a database of shapes with a translation and rotation invariant feature extractor. We retrieve silhouettes by using a “soft” classification based on the Euclidean distance. Experiments show significant gains in retrieval accuracy over the existing literature. This work extends the use of our previously employed feature extractor and...
Texture identification can be a key component in content based image retrieval systems. Although formal definitions of texture vary in the literature, it is commonly accepted that textures are naturally extracted and recognized as such by the human visual system, and that this analysis is performed in the frequency domain. The vast majority of the methods proposed in the literature provide good characterization...
The textured images are classified with a supervised segmentation algorithm (the classifier is trained first on texture samples). Texture features are extracted in the frequency domain and classified by a vector quantizer. Since feature vectors are computed and classified independently, for each pixel, a Kuwahara-like filter is used on the final classification to improve consistency. Feature vectors...
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