Image retrieval has been popular for several years. This paper propose a generalized query method of content based image retrieval system (CBIR) by using the several features and technique [1] i.e. color, texture, shape and GLCM, and also content based image retrieval system based on the combination of local and global features. The local features used are Bi-directional Empirical Mode Decompos...

palmprint recognition is a new biometrics system based on physiological characteristics of the palmprint, which includes rich, stable, and unique features such as lines, points, and texture. texture is one of the most important features extracted from low resolution images. in this paper, a new local descriptor, local composition derivative pattern (lcdp) is proposed to extract smartly stronger...

Image retrieval is an important research field which has received great attention in the last decades. In this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. For text-based features, keywords and for content-based features, color and texture features have been used. Query in this system contains some keywords and an input...

Saliency regions attract more human’s attention than other regions in an image. Low- level and high-level features are utilized in saliency region detection. Low-level features contain primitive information such as color or texture while high-level features usually consider visual systems. Recently, some salient region detection methods have been proposed based on only low-level features or hig...

Quality control is an important issue in the ceramic tile industry. Price of ceramic tiles also depends on purity of texture, accuracy of color, shape etc. Considering this criteria, an integrated defect detection and classification technique has been proposed which plays an important role in ceramic tiles industries to detect the defects and to control the quality of ceramic tiles.GLCM extract...

This paper presents a combined system for detecting masses in mammographic images. The proposed approach analyses the mammograms in three major steps. First a global segmentation method is applied to find the regions of interest. This step uses texture features, decision trees and a multiresolution Markov Random Field model. The second stage works on the output of the previous algorithm. Here a...

Real-life visual object recognition requires the processing of more than just geometric (shape, size, and orientation) properties. Surface properties such as color and texture are equally important, particularly for providing information about the material properties of objects. Recent neuroimaging research suggests that geometric and surface properties are dealt with separately within the late...

In current technology the acquisition, transmission, storing, and manipulation are allowed on the large collections of images. With the increase in popularity of the network and development of multimedia technologies, users are not satisfied with the traditional information retrieval techniques. So nowadays, the content based image retrieval is becoming a source of exact and fast retrieval. Con...

Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes, it is important to develop significant features for texture. This paper presents a novel technique for texture extraction and classification. The proposed feature extraction technique uses 2D–DFT transformation. A combi...