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...

Image indexing based on Modified Color Co-occurrence Matrix (MCCM) is proposed in this paper. First, CCM is simplified to represent the number of color (hue) pairs between adjacent pixels in the image. And then, CCM is split into diagonal and non-diagonal elements that constitute two elements of MCCM. Indexing the image by MCCM could exploit shape information in abstract level. Proposed MCCM is...

This work presents an approach for color-texture classification of industrial products. An extension of Gray Level Co-occurrence Matrix (GLCM) to color images is proposed. Statistical features are computed from an isotropic Color Co-occurrence Matrix for classification. The following color spaces are used: RGB, HSL and La*b*. New combination schemes for texture analysis are introduced. A compar...

This work aims to analyze the use of spatial information and the effectiveness of such information for tracking a person’s head in a video sequence. This thesis introduces new concepts that make use of color information along with a limited amount of spatial information on a global scale. Also, existing concepts like co-occurrence matrices are adapted to color images to provide a concept that u...

Since more than 50 years texture in image material is a topic of research. Hereby, color was ignored mostly. This study compares 70 different configurations for texture analysis, using four features. For the configurations we used: (i) a gray value texture descriptor: the co-occurrence matrix and a color texture descriptor: the color correlogram, (ii) six color spaces, and (iii) several quantiz...

We propose a new content-based image retrieval using a block color co-occurrence matrix (BCCM) and pattern correlogram. In the proposed method, the color feature vectors are extracted by using BCCM that represents the probability of the co-occurrence of two mean colors within blocks. Also the pattern feature vectors are extracted by using pattern correlogram which is combined with spatial corre...

In this study, an algorithm is proposed to determine the duration in a rice crop cycle based on texture analysis. During an observation period in 2013, daily images were acquired from a still camera installed at a paddy field. Given a set of time-series images, the texture analysis is used to classify different stages of the rice growing. Regarding the hypothesis, the rice crop cycle can be sep...

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We prop...

The paper presents a novel approach for representing color and intensity of pixel neighborhoods in an image using a co-occurrence matrix. After analyzing the properties of the HSV color space, suitable weight functions have been suggested for estimating relative contribution of color and gray levels of an image pixel. The suggested weight values for a pixel and its neighbor are used to construc...