With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-features, such as texture feature, are widely utilized in classification process to enhance classification accuracy greatly. In this paper, a novel...

Abstract: The pattern identification problems such as stone, rock categorization and wood recognition are used texture classification technique due to its valuable usage in it. Generally, texture analysis can be done one of the two ways i.e. statistical and structural approaches. More problems are occurred when working with statistical approaches in texture analysis for texture categorization. ...

The remote sensing technique has been widely use on the field of land cover classification. Traditionally, image classification is based on per-pixel which with the spectral of object. This kind of method do not considerate the spatial relationship between neighbor of pixels. Therefore many of study were focus of utilizes the texture information to distinguish between spatial characteristic. It...

Traditional spectral classification of remote sensing data applied on per pixel basis ignores the potentially useful spatial information between the values of proximate pixels. Although spatial information extraction has been greatly explored, there have been limited attempts to enhance classification by combining spectral and spatial information. This improvement would arise from the hypothesi...

The problem of automatic classification of ultrasound images is addressed. For texture analysis of ultrasound images quantifiable indexes, called features, are used. Classification was performed using Gaussian mixture model based on Bayes classifier. The common problem of texture analysis is a feature selection for classification tasks. In this work we use genetic algorithms for a feature subse...

We have developed an approach to the separation of background texture and structures in images. The developed approach is based on the statistical difference between local and median co-occurrence matrices. It is our assertion that the classification of mammographic parenchymal patterns can be improved if anatomical structures can be removed from the image and the classification is based only o...

Medical disease examination is often based on images. Mining these images in order to obtain the classification knowledge for automatic image classification is a challenging task. This task belongs to the field of image mining. Image mining is usually not only comprised of mining a table of numbers it has also to do with transforming the image in the right image description. Both, the image des...

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 ...