Conference Paper

Query by example using invariant features from the double dyadic dual-tree complex wavelet transform

DOI: 10.1145/1646396.1646403 Conference: Proceedings of the 8th ACM International Conference on Image and Video Retrieval, CIVR 2009, Santorini Island, Greece, July 8-10, 2009
Source: DBLP


Widespread use of digital imagery has resulted in a need to manage large collections of images. Systems providing query by example (QBE) capability offer improved access to contents of image libraries by retrieving matches to a query image. Texture is an important feature to consider in the matching process. However, standard approaches often employ a texture feature that is scale and rotation specific, and may not perform well in libraries containing images with scaled or rotated matches to the target query. A novel approach for generating scale and rotation invariant texture features from an extension of the Dual-Tree Complex Wavelet Transform (DT-CWT) is presented herein for use in region-based QBE. An experimental comparison reveals an improved ability of the new technique in retrieving relevant images over the standard approach.

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    • "The majority of scale-invariant methods perform similarly to non-invariant image representations however. This is true for methods based on the DT-CWT and the D 3 T-CWT [89] [72] (OCR 66-88), dense SIFT features [79] [60] [58] (OCR 83.5) as well as Multiscale Blob Features [98] [60] (OCR 86). "
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    Full-text · Article · Feb 2015 · Computers in Biology and Medicine
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    • "Now the original feature vector is cyclically shifted in the scale dimension, so that the first scale level of the new local feature vector is the scale level of the original local feature vector, in which the correlation vector had its maximum (see Fig. 6). Then the subbands (consisting of the corresponding feature values) are modeled by a Rayleigh distribution (Lo et al., 2009) and the parameters of this distribution are used to form the final feature vector of an image. Since we use only one statistical feature per subband, the number of features per image is half the number of features using the D 3 T-CWT (216). "
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