Fusion of Gabor Filter and Co-occurrence Probability Features for Texture Recognition
نویسندگان
چکیده
This paper explores a design-based method to fuse Gabor filter features and co-occurrence probability features for improved texture recognition. The fused feature set utilizes both the Gabor filter’s capability of accurately capturing lower frequency texture information and the co-occurrence probability’s capability in texture information relevant to higher frequency components. Fisher linear discriminant analysis indicates that the fused features have much higher feature space separation than the pure features. Image texture segmentation results are presented that also demonstrate the improvement using the fused feature sets.
منابع مشابه
Gabor Filters and Grey-level Co-occurrence Matrices in Texture Classification
Texture classification is a problem that has been studied and tested using different methods due to its valuable usage in various pattern recognition problems, such as wood recognition and rock classification. The Grey-level Co-occurrence Matrices (GLCM) and Gabor filters are both popular techniques used on texture classification. This paper combines both techniques in order to increase the acc...
متن کاملPalmprint Recognition Using PCA and Weighted Feature Level Fusion of 2D–Gabor and Log-Gabor Features
Palmprint biometric technology is most accurate and reliable, which has acquired good impact over the remaining biometric technologies. Palmprint contains various features like minutiae points, wrinkles, palm lines and texture, etc. A Number of line based approaches, subspace based approaches and texture based methods for extracting features from Palmprint have been considered and studied thoro...
متن کاملA Comparison of Texture Models for Automatic Liver Segmentation
Automatic liver segmentation from abdominal computed tomography (CT) images based on gray levels or shape alone is difficult because of the overlap in gray-level ranges and the variation in position and shape of the soft tissues. To address these issues, we propose an automatic liver segmentation method that utilizes low-level features based on texture information; this texture information is e...
متن کاملComparison and Fusion of Co-occurrence, Gabor andMRF Texture Features for Classification of SAR Sea-Ice Imagery
Image texture interpretation is an important aspect of the computer-assisted discrimination of Synthetic Aperture Radar (SAR) sea-ice imagery. Co-occurrence probabilities are the most common approach used to solve this problem. However, other texture feature extraction methods exist that have not been fully studied for their ability to interpret SAR sea-ice imagery. Gabor filters and Markov ran...
متن کاملInternational Journal of Advanced Studies in Computer Science & Engineering
Texture segmentation is the process of partitioning an image into regions with different textures containing a similar group of pixels. Detecting the discontinuity of the filter's output and their statistical properties help in segmenting and classifying a given image with different texture regions. In this proposed paper, chili x-ray image texture segmentation is performed by using Gabor filte...
متن کامل