Name
Title | ||
|---|---|---|
Improved opponent color local binary patterns: an effective local image descriptor for color texture classification. |
Abstract | ||
|---|---|---|
Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intraand interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images. (c) 2017 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1117/1.JEI.27.1.011002 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
local binary patterns,image classification,color texture,convolutional neural networks | Computer vision,Pattern recognition,Convolutional neural network,Feature (computer vision),Computer science,Local color,Local binary patterns,Concatenation,Artificial intelligence,Thresholding,Contextual image classification,Grayscale | Journal |
Volume | Issue | ISSN |
27 | 1 | 1017-9909 |
Citations | PageRank | References |
5 | 0.41 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francesco Bianconi | 1 | 22 | 2.50 |
| Raquel Bello-Cerezo | 2 | 12 | 1.86 |
| Paolo Napoletano | 3 | 339 | 37.19 |