.png){kind=icon}
.png){kind=icon}
Return
Experiments in colour texture analysis
.png){kind=icon}
OA
AI
DOI：10.1016/S0167-8655(01)00058-7.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we focus on the classification of colour texture images. The main objective is to determine the contribution of colour information to the overall classification performance. Three relevant approaches to grey scale texture analysis, namely local linear transforms, Gabor filtering and the co-occurrence approach are extended to colour images. They are evaluated in a quantitative manner by means of a comparative experiment on a set of colour images. We also investigate the effect of using different colour spaces and the contribution of colour and texture features separately and collectively. The evaluation criteria is the classification accuracy using a neural network classifier based on Learning Vector Quantization. Experimental results indicate that the incorporation of colour information enhances the performance of the texture analysis techniques examined. (C) 2001 Elsevier Science B.V. All rights reserved.
Keywords:
texture
colour
learning vector quantization
classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.8K
Citations:
1.6W
Organization
No organization information available