Image classification can benefit from incorporating texture by enabling an increased number of classes and improving thematic accuracy. Incorporating texture also involves special attention in a number of aspects that range from the texture source to the evaluation of accuracy through pre-processing, training strategy and choosing a texture extraction paradigm and a classifier. Without special ...

The objective of the present work is to evaluate the potential of the use of the image fractal dimension as textural feature in a content-based image retrieval system. In order to compare and classify the regions of an image, we have used two distances between two partitions of the image: (1) a classic distance computed with some features (contrast, energy, entropy, homogeneity, correlation) ex...

The quality of pharmaceutical products plays an important role in pharmaceutical industry as well as in our lives. Usage of defective tablets can be harmful for patients. In this research we proposed a nondestructive method to identify defective and nondefective tablets using their surface morphology. Three different environmental factors temperature, humidity and moisture are analyzed to evalu...

Texture as a measure of spatial features has been useful as supplementary information to improve image classification in many areas of research fields. This study focuses on assessing the ability of different textural vectors and their combinations to aid spectral features in the classification of silicate rocks. Texture images were calculated from Landsat 8 imagery using a fractal dimension me...

For diagnosis of lung tumors, CT scan of lungs is one of the most common imaging modalities. Manually identifying tumors from hundreds of CT image slices for any patient may prove to be a tedious and time consuming task for the radiologists. Therefore, to assist the physicians we propose an automatic lung tumor detection method based on textural features. The lung parenchyma region is segmented...

In this article, a fabulous method for database retrieval is proposed.  The multi-resolution modified wavelet transform for each of image is computed and the standard deviation and average are utilized as the textural features. Then, the proposed modified bit-based color histogram and edge detectors were utilized to define the high level features. A feedback-based dynamic weighting of shap...

This paper presents two-stage unsupervised terrain classification of fully polarimetric SAR data using Freeman and Durden decomposition based on three simple scattering mechanisms: surface, volume and double bounce (first step), and textural features (uncorrelated uniformity, contrast, inverse moment and entropy) obtained from grey level co-occurrence matrices (GLCM) (second step). Textural fea...