A multi-scale supervised orientational invariant neural architecture for natural texture classification

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

“…Texture pattern classification was considered an important problem in computer vision for many years because of the great variety of possible applications, including non-destructive inspection of abnormalities on wood, steel, ceramics, fruit, and aircraft surfaces [1][2][3][4][5][6]. Texture discrimination remains a challenge since the texture of objects varies significantly according to the viewing angle, illumination conditions, scale change, and rotation [1,4,7,8]. There is also the special problem of color image retrieval related to appearance-based object recognition, which is a major field of development for several industrial vision applications [1,4,7,8].…”

“…Texture discrimination remains a challenge since the texture of objects varies significantly according to the viewing angle, illumination conditions, scale change, and rotation [1,4,7,8]. There is also the special problem of color image retrieval related to appearance-based object recognition, which is a major field of development for several industrial vision applications [1,4,7,8].…”

“…Determining the appropriate features for each problem is a recurring challenge which is yet to be fully met by the computer vision community [1,3,16]. Feature extraction and selection enable representation of the information present in the image, and limit the number of features, thus allowing further analysis within a reasonable time.…”

“…Feature extraction was used in a wide range of applications, such as biometrics [12,14,15], classification of cloth, surfaces, landscapes, wood, and rock minerals [16,17], saliency detection [18], and background subtraction [19], among others. During the past 40 years, while a substantial number of methods for grayscale texture classification were developed [3,5], there was also a growing interest in colored textures [1,2,9,10,13,20,21]. The adaptive integration of color and texture attributes into the development of complex image descriptors is an area of intensive research in computer vision [21].…”

“…There are other methods that reached the best results in color texture analysis on databases that are publicly available. In Reference [1], a multi-scale architecture (Multi-Scale Supervised self-Organizing Orientational-invariant Neural or multi-scale SOON) was presented that reached 91.03% accuracy on the Brodatz database, which contains 111 different colored textures. These results were compared with those of two previous studies on the same database, reported in References [43,44], which reached classification rates of 89.71% and 88.15%, respectively.…”