Journal ArticleDOI
Texture image retrieval using rotated wavelet filters
TLDR
A novel approach for texture image retrieval is proposed by using a new set of two-dimensional rotated wavelet filters (RWF) and discrete wavelet transform (DWT) jointly, which improves retrieval rate and retains comparable levels of computational complexity.About:
This article is published in Pattern Recognition Letters.The article was published on 2007-07-01. It has received 160 citations till now. The article focuses on the topics: Wavelet packet decomposition & Stationary wavelet transform.read more
Citations
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Journal ArticleDOI
Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval
TL;DR: A novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR) that encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order derivatives in vertical and horizontal directions.
Journal ArticleDOI
Image retrieval based on micro-structure descriptor
TL;DR: The results demonstrate that the proposed micro-structure descriptor is much more efficient and effective than representative feature descriptors, such as Gabor features and multi-textons histogram, for image retrieval.
Journal ArticleDOI
An effective image retrieval scheme using color, texture and shape features
TL;DR: A new and effective color image retrieval scheme for combining all the three i.e. color, texture and shape information, which achieved higher retrieval efficiency and provides a robust feature set for image retrieval.
Journal ArticleDOI
Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking
TL;DR: A new algorithm meant for content based image retrieval (CBIR) and object tracking applications is presented, which differs from the existing LBP in a manner that it extracts the information based on distribution of edges in an image.
Journal ArticleDOI
Directional local extrema patterns: a new descriptor for content based image retrieval
TL;DR: A new algorithm using directional local extrema patterns meant for content-based image retrieval application that shows a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases.
References
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Journal ArticleDOI
A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
Book
Ten lectures on wavelets
TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
Journal ArticleDOI
Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
Journal ArticleDOI
Receptive fields, binocular interaction and functional architecture in the cat's visual cortex
David H. Hubel,Torsten N. Wiesel +1 more
TL;DR: This method is used to examine receptive fields of a more complex type and to make additional observations on binocular interaction and this approach is necessary in order to understand the behaviour of individual cells, but it fails to deal with the problem of the relationship of one cell to its neighbours.
Journal ArticleDOI
Content-based image retrieval at the end of the early years
TL;DR: The working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap are discussed, as well as aspects of system engineering: databases, system architecture, and evaluation.
Related Papers (5)
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
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