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: The advent of large scale multimedia databases has led to great challenges in content-based image retrieval (CBIR). Even though CBIR is considered an emerging field of research, however it constitutes a strong background for new methodologies and systems implementations. Therefore, many research contributions are focusing on techniques enabling higher image retrieval accuracy while preserving low level of computational complexity. Image retrieval based on texture features is receiving special attention because of the omnipresence of this visual feature in most real-world images. This paper highlights the state-of-the-art and current progress relevant to texture-based image retrieval and spatial-frequency image representations. In particular, it gives an overview of statistical methodologies and techniques employed for texture feature extraction using most popular spatial-frequency image transforms, namely discrete wavelets, Gabor wavelets, dual-tree complex wavelet and contourlets. Indications are also given about used similarity measurement functions and most important achieved results. Comment: 19 pages, 11 figures, 2 tables12/2010;
Conference Paper: Scale and Rotation Invariant Gabor Features for Texture Retrieval[Show abstract] [Hide abstract]
ABSTRACT: For image classification applications it is often useful to generate a compact representation of the texture of an image region. The conventional representation of image textures using extracted Gabor wavelet coefficients often yields poor performance when classifying scaled and rotated versions of image regions. In this paper we propose a scale and rotation invariant feature generation procedure for classification and of images using Gabor filter banks. Firstly, to obtain scale and rotation invariant features, each image is decomposed at different scales and orientations. Then, in order to create unique feature vectors, we apply a circular shift operation to both scale and rotation dimensions to shift the maximum value of the Gabor filters to the first orientation of the first scale and the energies of these filtered images are calculated. To demonstrate the effectiveness of our proposed approach we compare its performance with the most recent texture feature generation methods in a classification task. Experimental results show that our proposed feature generation method is more accurate at classifying scaled and rotated textures than the existing methods.Digital Image Computing Techniques and Applications (DICTA), 2011 International Conference on; 12/2011
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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