Analysis and Optimization of Feature Extraction Techniques for Content Based Image Retrieval

2016 ◽  
pp. 357-365
Author(s):  
Kavita Chauhan ◽  
Shanu Sharma
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Extraction Techniques

scholarly journals Content Based Image Retrieval using Texture, Color and Shape for Image Analysis

2012 ◽  
Vol 3 (1) ◽  
pp. 149-152 ◽  
Author(s):  
Amanbir Sandhu ◽  
Aarti Kochhar
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Extraction Techniques ◽  
Low Level ◽  
The Past ◽  
Single Feature ◽  
Color Texture

Content- Based Image Retrieval(CBIR) or QBIR  is the important  field of research..Content  Based Image retrieval has gained much popularity  in the past Content-based image retrieval (CBIR)[1] system has also helped users to retrieve relevant images based on their contents. It represents low level features like texture ,color and shape .In this paper, we compare the several feature extraction techniques [5]i.e..GLCM ,Histogram and shape properties  over color,  texture and shape The experiments show the similarity between these features and also that the output obtained using this combination of color, texture and shape is better as obtaining output  with a single feature

scholarly journals Content Based Image Retrieval: A Review

2020 ◽  
Vol 9 (10) ◽  
pp. 222-228
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Features ◽  
Image Databases ◽  
Image Recovery ◽  
Content Based Image Retrieval ◽  
Science Content ◽  
Extraction Techniques ◽  
Vision Science ◽  
Retrieval Systems

Image recovery was one of the most thrilling and vibrant fields of computer vision science. Content-based image retrieval systems (CBIR) are used to catalog, scan, download and access image databases automatically. Color & texture features are significant properties for content-based image recovery systems. The content-based image retrieval (CBIR) is therefore an attractive source of accurate and quick retrieval. Number of techniques has been established in recent years to improve the performance of CBIR. This paper discusses why CBIR is important nowadays along with the limitations and benefits. Apart from applications, various feature extraction techniques used in CBIR are also discussed.

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.

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

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