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

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 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

On Hierarchical Content-Based Image Retrieval by Dynamic Indexing and Guided Search

2010 ◽  
Vol 4 (4) ◽  
pp. 18-36 ◽  
Author(s):  
Jane You ◽  
Qin Li ◽  
Jinghua Wang
Keyword(s):  
Image Retrieval ◽  
Data Warehouse ◽  
Image Data ◽  
Image Feature ◽  
Image Indexing ◽  
Content Based Image Retrieval ◽  
New Approach ◽  
Guided Search ◽  
Dynamic Image ◽  
Face Features

This paper presents a new approach to content-based image retrieval by using dynamic indexing and guided search in a hierarchical structure, and extending data mining and data warehousing techniques. The proposed algorithms include a wavelet-based scheme for multiple image feature extraction, the extension of a conventional data warehouse and an image database to an image data warehouse for dynamic image indexing. It also provides an image data schema for hierarchical image representation and dynamic image indexing, a statistically based feature selection scheme to achieve flexible similarity measures, and a feature component code to facilitate query processing and guide the search for the best matching. A series of case studies are reported, which include a wavelet-based image color hierarchy, classification of satellite images, tropical cyclone pattern recognition, and personal identification using multi-level palmprint and face features. Experimental results confirm that the new approach is feasible for content-based image retrieval.

Sign in / Sign up

Export Citation Format

Share Document