scholarly journals SMART CITY SECURITY: FACE-BASED IMAGE RETRIEVAL MODEL USING GRAY LEVEL CO-OCCURRENCE MATRIX

2020 ◽  
Vol 19 (3) ◽  
pp. 437-458 ◽  
Author(s):  
Abdullah Mohammed Rashid ◽  
Ali Adil Yassin ◽  
Ahmed Adel Abdel Wahed ◽  
Abdulla Jassim Yassin
Keyword(s):  
Image Retrieval ◽  
Smart City ◽  
Gray Level ◽  
Retrieval Model ◽  
Occurrence Matrix

scholarly journals Using Color and Texture Feature Extraction Technique to Retrieve Image

2019 ◽  
Vol 8 (11S) ◽  
pp. 915-917
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Color Images ◽  
Color Histogram ◽  
Gray Level ◽  
Texture Feature Extraction ◽  
Color Feature ◽  
Occurrence Matrix ◽  
Feature Extraction Technique

This paper proposes a content image retrieval using the texture and the color feature of the images. Although for extraction of texture feature, the “gray level co-occurrence matrix (GLCM) algorithm” is used and for extracting color feature the color histogram is used. The presented system is tested on the WANG database that contains a thousand color images with ten different classes by the help of three various type of distances

Research on Characteristic Properties of Gray Level Co-Occurrence Matrix

2012 ◽  
Vol 204-208 ◽  
pp. 4755-4759
Author(s):  
Ying Chen ◽  
Feng Yu Yang
Keyword(s):  
Image Retrieval ◽  
Change Process ◽  
Second Order ◽  
Experimental Results ◽  
Moving Window ◽  
Gray Level ◽  
Dynamic Changes ◽  
Occurrence Matrix ◽  
Level Quantization ◽  
Secondary Diagonal

Gray level co-occurrence matrix (GLCM) is a second-order statistical measurement. In order to understand the characterization degree of GLCM’s different feature properties, we use images of Brodatz texture images as experimental samples, analyze the change process of feature properties in horizontal, vertical and principal and secondary diagonal directions under the situation of some elements’ dynamic changes such as distance of pixels pair, size of moving window and gray level quantization,. By analyzing the experimental results, this paper can provided certain referential significance in how to select feature properties reasonable in the application of image retrieval and classification and identification which are based on using GLCM as feature.

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