in this paper, the performance of 11 different distances for image retrieval and classification, based on color, shape and texture, is evaluated. the precision-recall measure and the correct classification rate of the k-nn classifier are used to evaluate retrieval and classification performances, respectively. the experimental results for a database of 1000 images from 10 different semantic gro...

The Applications of Pattern recognition like wood classification, stone and rock classification problems, the major usage techniques ate different texture classification techniques. Generally most of the problems used statistical approach for texture analysis and texture classification. Gray Level Co-occurrence Matrices (GLCM) approach is particularly applied in texture analysis and texture cla...

In this paper we have investigated a new approach for texture features extraction using co-occurrence matrix from volumetric lung CT image. Traditionally texture analysis is performed in 2D and is suitable for images collected from 2D imaging modality. The use of 3D imaging modalities provide the scope of texture analysis from 3D object and 3D texture feature are more realistic to represent 3D ...

Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for analysis manipulation spectrum of This paper employs the Gaussian–Laplacian to separately treat different frequency bands texture. First, we generate three images corresponding levels input capture intrinsic details. Then, aggregate features e...

introduction the increasing use of satellite remote sensing data for civilian use has proved to be the most cost-effective means of mapping and monitoring for environmental changes. satellite remote sensing has played a pivotal role in finding forest cover, vegetation type and land use changes in urban areas. one of the most complete of these methods is classification. the conventional per-pixe...

The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These...