Research And Application Of The Texture Features Extraction Based On Gabor Filter

Texture, as a very important property of nature and visualization on the surface of theobject, compared with other properties of the image which has its own advantages. It will notchange as the changes of color, brightness of the object images, so it plays an important rolein the field of image analysis.And most critical step in the field of texture analysis is theextraction of texture feature, texture feature extraction method based on Gabor filter, has itsincomparable advantage of other methods.This topic by texture image as the research object, is conducted in-depth research oftexture image for feature extraction based on Gabor filter. Specifically, the main researchcontent of this topic includes the followings:1.Some theories and algorithms based on the Gabor filter is studied, focusing on theadvantages and disadvantages of Gabor transform, wavelet transform, Gabor wavelettransform are analyzed, finally aimed at the characteristics of these three kinds of filter issimulated, and the simulation results were discussed, showing the advantages of Gaborwavelet filter.2. This paper uses the Gabor wavelet filter for feature extraction of texture image, studiesthe algorithms of texture feature extraction based on traditional Gabor filter and analyzes itsadvantages and defects. On the basis of in-depth study, we improved the defects of traditionalmethods that exist large computation and time-consuming, then improved algorithm is putforward, and two algorithms are simulated. Finally, the traditional algorithm and improvedalgorithm were compared and analyzed, the results show that the improved algorithm takesless time, the extracted results also performed better than the original algorithm3. Applying the improvement of texture feature extraction algorithm based on Gabor filter to the texture segmentation, and selecting pieces image texture map that combined into acomposite from Brodatz texture library, execute the texture segmentation of composite imageby using k-means segmentation algorithm. By observing its results, found that the improvedalgorithm segmentation result is satisfactory.