Adaptive tetrolet based color, texture and shape feature extraction for content based image retrieval application

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Jitesh Pradhan ◽  
Arup Kumar Pal
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Shape Feature ◽  
Shape Feature Extraction ◽  
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

Review of image low-level feature extraction methods for content-based image retrieval

Sensor Review ◽  
2019 ◽  
Vol 39 (6) ◽  
pp. 783-809
Author(s):  
Shenlong Wang ◽  
Kaixin Han ◽  
Jiafeng Jin
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Extraction Methods ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Image Feature Extraction ◽  
Content Type ◽  
Low Level ◽  
Feature Expression ◽  
Color Texture

Purpose In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years. Design/methodology/approach First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared. Findings The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR. Originality/value A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

scholarly journals Design of Feature Extraction in Content Based Image Retrieval (CBIR) using Color and Texture

2012 ◽  
pp. 157-161
Author(s):  
Swati V. Sakhare ◽  
Vrushali G. Nasre
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Image Database ◽  
Color Histogram ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Efficient Approach ◽  
Novel Method ◽  
Fuzzy Color Histogram ◽  
Color Texture

Retrieval of images based on visual features such as color, texture and shape have proven to have its own set of limitations under different conditions. Various techniques have been implemented using these features like fuzzy color histogram, Tammura texture etc. In this paper we propose a novel method with highly accurate and retrieval efficient approach which will work on large image database with varied contents and background.

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

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