The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
A sparse texture representation using local affine regions
Open accessAbstract
This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the image. Each of these regions can be thought of as a texture element having a characteristic elliptic shape and a distinctive appearance pattern. This pattern is captured in an affine-invariant fashion via a process of shape normalization followed by the computation of two novel descriptors, the spin image and the RIFT descriptor. When affine invariance is not required, the original elliptical shape servee as an additional discriminative feature for texture recognition. The proposed approach is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.
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Cites
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Cited by
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503
A comparison of affine region detectors
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Self-citationDeep Unsupervised Learning using Nonequilibrium Thermodynamics
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Describing Textures in the Wild
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A Completed Modeling of Local Binary Pattern Operator for Texture Classification
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Metadata
- Authors
- Lazebnik, S., Schmid, C., & Ponce, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2005-08
- Volume
- 27
- Issue
- 8
- Pages
- 1265-1278
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2126833203
- Cited by
- 1,123
- References
- 51
- FWCI
- 33.11
- Type
- journal article
- Language
- en
- Open access
- green
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