Share Email Print
cover

Proceedings Paper

Scene classification of infrared images based on texture feature
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Scene Classification refers to as assigning a physical scene into one of a set of predefined categories. Utilizing the method texture feature is good for providing the approach to classify scenes. Texture can be considered to be repeating patterns of local variation of pixel intensities. And texture analysis is important in many applications of computer image analysis for classification or segmentation of images based on local spatial variations of intensity. Texture describes the structural information of images, so it provides another data to classify comparing to the spectrum. Now, infrared thermal imagers are used in different kinds of fields. Since infrared images of the objects reflect their own thermal radiation, there are some shortcomings of infrared images: the poor contrast between the objectives and background, the effects of blurs edges, much noise and so on. Because of these shortcomings, it is difficult to extract to the texture feature of infrared images. In this paper we have developed an infrared image texture feature-based algorithm to classify scenes of infrared images. This paper researches texture extraction using Gabor wavelet transform. The transformation of Gabor has excellent capability in analysis the frequency and direction of the partial district. Gabor wavelets is chosen for its biological relevance and technical properties In the first place, after introducing the Gabor wavelet transform and the texture analysis methods, the infrared images are extracted texture feature by Gabor wavelet transform. It is utilized the multi-scale property of Gabor filter. In the second place, we take multi-dimensional means and standard deviation with different scales and directions as texture parameters. The last stage is classification of scene texture parameters with least squares support vector machine (LS-SVM) algorithm. SVM is based on the principle of structural risk minimization (SRM). Compared with SVM, LS-SVM has overcome the shortcoming of higher computational burden by solving linear equations, and has been widely used in classification and nonlinear function estimation. Some experimental results are given in the end. The result shows that Gabor wavelet transform is successful to extract the texture feature of infrared image. Compared with other methods the method mentioned in this paper reduces the probability of recognition and enhances the robustness.

Paper Details

Date Published: 28 January 2009
PDF: 8 pages
Proc. SPIE 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, 715626 (28 January 2009); doi: 10.1117/12.806945
Show Author Affiliations
Xiao Zhang, Beijing Institute of Technology (China)
Tingzhu Bai, Beijing Institute of Technology (China)
Fei Shang, Jilin Univ. (China)


Published in SPIE Proceedings Vol. 7156:
2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments

© SPIE. Terms of Use
Back to Top