A Moment Based Feature Extraction for Texture Image Retrieval

2019 ◽  
pp. 167-177 ◽  
Author(s):  
Ivy Majumdar ◽  
B. N. Chatterji ◽  
Avijit Kar
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Image

scholarly journals A Study on Image Retrieval Based on Tetrolet Transform

2018 ◽  
Vol 7 (3.27) ◽  
pp. 321
Author(s):  
Devika Sarath ◽  
M Sucharitha
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Computation Time ◽  
Extraction Methods ◽  
Distance Measures ◽  
Computational Time ◽  
Multimedia Database ◽  
Texture Image ◽  
Survey Paper

Retrieving images from the large databases has always been one challenging problem in the area of image retrieval while maintaining the higher accuracy and lower computational time. Texture defines the roughness of a surface. For the last two decades due to the large extent of multimedia database, image retrieval has been a hot issue in image processing. Texture images are retrieved in a variety of ways. This paper presents a survey on various texture image retrieval methods. It provides a brief comparison of various texture image retrieval methods on the basis of retrieval accuracy and computation time. Image retrieval techniques vary with feature extraction methods and various distance measures. In this paper, we present a survey on various texture feature extraction methods by applying tertrolet transform. This survey paper facilitates the researchers with background of progress of image retrieval methods that will help researchers in the area to select the best method for texture image retrieval appropriate to their requirements.

A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier

2014 ◽  
Vol 40 (8) ◽  
pp. 154-162 ◽  
Author(s):  
Shailendrakumar M. Mukane ◽  
Sachin R. Gengaje ◽  
Dattatraya S. Bormane
Keyword(s):  
Fuzzy Logic ◽  
Image Retrieval ◽  
Texture Image ◽  
Rotation Invariant ◽  
Retrieval Method

scholarly journals A novel content based image retrieval system using K-means/KNN with feature extraction

2012 ◽  
Vol 9 (4) ◽  
pp. 1645-1661 ◽  
Author(s):  
Ray-I Chang ◽  
Shu-Yu Lin ◽  
Jan-Ming Ho ◽  
Chi-Wen Fann ◽  
Yu-Chun Wang
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Color Analysis ◽  
Image Retrieval System ◽  
First Time ◽  
System Designs

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

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