Content-Based Image Retrieval Using Integrated Color, Texture, and Shape Features

2017 ◽  
pp. 257-271 ◽  
Author(s):  
Vipin Tyagi
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Color Texture

Content-Based Image Retrieval Using Color, Texture and Shape Features

2016 ◽  
Vol 685 ◽  
pp. 872-876 ◽  
Author(s):  
Alexey Ponomarev ◽  
Hitesh S. Nalamwar ◽  
Ilya Babakov ◽  
Chandrakant S. Parkhi ◽  
Gaurav Buddhawar
Keyword(s):  
Image Retrieval ◽  
Digital Imaging ◽  
Imaging Techniques ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
Large Databases ◽  
The Given ◽  
Color Texture

Nowadays CBIR is getting more and more attention from organizations and researchers due to advances in digital imaging techniques. A lot of interest is getting paid to search images from large databases, as it is not only difficult and time-consuming task but sometimes frustrating for the users. This paper proposes the CBIR system based on color, texture and shape features. The proposed method employs the use of DCT and DWT along with Hierarchical k-means algorithm for faster retrieval of images. The efficiency of the given method is demonstrated by the results in the paper.

scholarly journals New Approach on the Techniques of Content-Based Image Retrieval (CBIR) Using Color, Texture and Shape Features

2021 ◽  
Vol 09 (01) ◽  
pp. 51-57
Author(s):  
Mohd Afizi Mohd Shukran ◽  
Muhamad Naim Abdullah ◽  
Mohd Sidek Fadhil Mohd Yunus
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Shape Features ◽  
New Approach ◽  
Color Texture

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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