scholarly journals WAVELET BASED CONTENT BASED IMAGE RETRIEVAL USING COLOR AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL COOCURENCE MATRIX AND COLOR COOCURENCE MATRIX

2014 ◽  
Vol 10 (1) ◽  
pp. 15-22 ◽  
Author(s):  
Jeyanthi Prabhu ◽  
Jawahar Senthil Kumar
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Gray Level ◽  
Texture Feature Extraction

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

AN INTELLIGENT HYBRID APPROACH FOR CONTENT-BASED IMAGE RETRIEVAL

2002 ◽  
Vol 02 (02) ◽  
pp. 173-184 ◽  
Author(s):  
S. KULKARNI ◽  
B. VERMA
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Hybrid Approach ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Multi Layer Perceptron ◽  
Texture Feature Extraction ◽  
Benchmark Database ◽  
Hidden Layer ◽  
Auto Associative Neural Network

The paper presents an intelligent hybrid approach for content-based image retrieval based on texture feature. The proposed approach employs an Auto–Associative Neural Network (AANN) for feature extraction and a Multi–Layer Perceptron (MLP) with a single hidden layer for the classification. Two intelligent approaches such as AANN–MLP and statistical–MLP were investigated. The performance of the proposed approaches was evaluated on a large benchmark database of texture patterns. The results are very promising compared to other existing traditional and intelligent techniques. Some of the experimental results conducted during the investigation, comparative analysis of the results and suggestions to select the appropriate techniques for texture feature extraction and classification are presented in this paper.

scholarly journals Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

2020 ◽  
Vol 9 (3) ◽  
pp. 1466-1469
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Attribute Extraction ◽  
The Individual ◽  
Selection Of ◽  
Color Texture

Selection of feature extraction method is incredibly recondite task in Content Based Image Retrieval (CBIR). In this paper, CBIR is implemented using collaboration of color; texture and shape attribute to improve the feature discriminating property. The implementation is divided in to three steps such as preprocessing, features extraction, classification. We have proposed color histogram features for color feature extraction, Local Binary Pattern (LBP) for texture feature extraction, and Histogram of oriented gradients (HOG) for shape attribute extraction. For the classification support vector machine classifier is applied. Experimental results show that combination of all three features outperforms the individual feature or combination of two feature extraction techniques

Sign in / Sign up

Export Citation Format

Share Document