Query by example using invariant features from the double dyadic dual-tree complex wavelet transform.
ABSTRACT 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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ABSTRACT: Scale invariant texture recognition methods are applied for the computer assisted diagnosis of celiac disease. In particular, emphasis is given to techniques enhancing the scale invariance of multi-scale and multi-orientation wavelet transforms and methods based on fractal analysis. After fine-tuning to specific properties of our celiac disease imagery database, which consists of endoscopic images of the duodenum, some scale invariant (and often even viewpoint invariant) methods provide classification results improving the current state of the art. However, not each of the investigated scale invariant methods is applicable successfully to our dataset. Therefore, the scale invariance of the employed approaches is explicitly assessed and it is found that many of the analyzed methods are not as scale invariant as they theoretically should be. Results imply that scale invariance is not a key-feature required for successful classification of our celiac disease dataset.Medical image analysis 02/2013; · 3.09 Impact Factor
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ABSTRACT: In this paper, scale invariant features are extracted from different variants of the Dual-Tree Complex Wavelet Transform (DT-CWT) in order to classify high-magnification colon endoscopy imagery with respect to the pit pattern scheme. To enhance the scale invariance, the Discrete Cosine Transform is applied to the feature vectors, that are achieved from a DT-CWT variant. The feature vectors either consist of the means and standard deviations of the subbands from a DTC-WT variant or of the Weibull parameter of these subbands. Superior results as compared to techniques described previously in literature are reported.01/2010;
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ABSTRACT: With the exponential growth of video data on the World Wide Web comes the challenge of efficient methods in video content management, content-based video search, filtering and browsing. But, video data often lacks sufficient meta-data to open up the video content and to enable pinpoint content-based search. With the advent of the 'web of data' as an extension of the current WWW new data sources can be exploited by semantically interconnecting video meta-data with the web of data. Thus, enabling better access to video repositories by deploying semantic search technologies and improving the user's search experience by supporting exploratory search strategies. We have developed the pro-totype semantic video search engine 'yovisto' that demon-strates the advantages of semantically enhanced exploratory video search and enables investigative navigation and brows-ing in large video repositories.01/2010;