An Innovative Technique of Texture Classification and Comparison Based on Long Linear Patterns

Abstract

The present paper proposes a method of texture classification based on long linear patterns. Linear patterns of long size are bright features defined by morphological properties: linearity, connectivity, width and by a specific Gaussian-like profile whose curvature varies smoothly along the crest line. The most significant information of a texture often appears in the occurrence of grain components. That’s why the present paper used sum of occurrence of grain components for feature extraction. The features are constructed from the different combination of long linear patterns with different orientations. These features offer a better discriminating strategy for texture classification. Further, the distance function captured from the sum of occurrence of grain components of textures is expected to enhance the class seperability power. The class seperability power of these features is investigated in the classification experiments with arbitrarily chosen texture images taken from the Brodatz album. The experimental results indicated good analysis, and how the classification of textures will be effected with different long linear patterns

Image Retrieval using Quantized Local Binary Pattern

“…The retrieval performance of the integrated Q-LBP is compared with HCA [46], CBIR-C [47] and FCMC [48] methods. The present paper selected 60 images of the same category or class as query images (one by one) and computed precession and recall rates by selecting top 16,20,30,40,50,60,70,80 and 90 images. The average precession rates of HCA, CBIR-C and FCMC methods are ranging from 38% to 45%, 39% to 46% and 60% to 64% respectively and for number of images retrieved is 16 ( Table 2).…”

“…Texture plays an important role in image classification, retrieval, segmentation and age classification. In Literature various texture based methods are derived for image classification [ 15,16,17], face recognition [18,19,20], age and facial expressions identification [21,22], segmentation [23,24,25] image retrieval [26,27,28,29,30,31,32 ]. Texture is salient and indispensable feature for CBIR.…”

Stone Texture Classification and Discrimination by Edge Direction Movement

“…A number of algorithms are designed for feature extraction of a texture image from the past 2 decades [7,8]. Other approaches for texture classification are marble texture description [9], skeleton extraction of texture [10], long linear patterns using wavelets [11] wavelet transform [11, 12, [22]. From the above literature, observe that no study has attempted to classify the textures with good classification results by using edge movement pattern approach with good classification results.…”

Morphological Shape features for Classification of Textures based on Fuzzy Texture Element

“…The study of patterns on textures is recognized as an important step in characterization and recognition of texture [17,18,24,25,26]. Various approaches are in use to investigate the textural and spatial structural characteristics of image data, including measures of texture [8], Fourier analysis [9,10], fractal dimension [11], variograms [12,13] and local variance measures [14].…”