Color and Texture Feature For Content Based Image Retrieval

2010 ◽  
Vol 4 (3) ◽  
pp. 43-49 ◽  
Author(s):  
Jianhua Wu ◽  
Zhaorong Wei ◽  
Youli Chang
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Content Based 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

scholarly journals Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 135608-135629
Author(s):  
Ayesha Khan ◽  
Ali Javed ◽  
Muhammad Tariq Mahmood ◽  
Muhammad Hamza Arif Khan ◽  
Ik Hyun Lee
Keyword(s):  
Image Retrieval ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Feature Descriptor

scholarly journals Content Based Image Retrieval using Multi-level 3D Color Texture and Low Level Color Features with Neural Network Based Classification System

2021 ◽  
Vol 15 ◽  
pp. 265-270
Author(s):  
Priyesh Tiwari ◽  
Shivendra Nath Sharan ◽  
Kulwant Singh ◽  
Suraj Kamya
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Multi Level ◽  
Different Color ◽  
Color Texture

Content based image retrieval (CBIR), is an application of real-world computer vision domain where from a query image, similar images are searched from the database. The research presented in this paper aims to find out best features and classification model for optimum results for CBIR system.Five different set of feature combinations in two different color domains (i.e., RGB & HSV) are compared and evaluated using Neural Network Classifier, where best results obtained are 88.2% in terms of classifier accuracy. Color moments feature used comprises of: Mean, Standard Deviation,Kurtosis and Skewness. Histogram features is calculated via 10 probability bins. Wang-1k dataset is used to evaluate the CBIR system performance for image retrieval.Research concludes that integrated multi-level 3D color-texture feature yields most accurate results and also performs better in comparison to individually computed color and texture features.

Images Retrieval Based on Integrated Features

2014 ◽  
Vol 543-547 ◽  
pp. 2292-2295
Author(s):  
Ching Hung Su ◽  
Huang Sen Chiu ◽  
Mohd Helmy A. Wahab ◽  
Tsai Ming Hsiehb ◽  
You Chiuan Li ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Texture Feature ◽  
Content Based Image Retrieval ◽  
Code Comparison ◽  
Retrieval Scheme ◽  
Image Retrieval System ◽  
Retrieval Problem ◽  
Color Sequence ◽  
Edge Histogram

We propose a practical image retrieval scheme to retrieve images efficiently. The proposed scheme transfers each image to a color sequence using straightforward 8 rules. Subsequently, using the color sequences to compare the images, namely color sequences comparison. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Edge Histogram Descriptor to compare the images of database. We succeed in transferring the image retrieval problem to quantized code comparison. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues both of the content based image retrieval system and a text based image retrieval system.

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