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 Relevance Feedback berdasarkan Support Vector Machine pada Content Based Image Retrieval

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Virginia Tulenan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Content Based Image Retrieval ◽  
Support Vector ◽  
Relevant Feedback

Content based image retrieval adalah bidang penelitianyang sangat penting saat ini dalam bidang multimedia database.Banyak penelitian yang telah dilakukan dalam dekade terakhiruntuk merancang teknik image retrieval yang efisien dari imagedatabase. Meskipun banyak teknik pengindeksan dan retrievaltelah dikembangkan, namun masih belum terdapat teknikpemisahan ciri (feature extraction), indexing dan retrieval yangbisa diterima secara universal oleh semua orang. Dalam tulisanini, digunakanlah metode relevant feedback berdasarkan supportvector machine (SVM) dan muhalobis distance untuk pengukurankemiripan pada image retrieval.

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.

scholarly journals A comprehensive survey on content based image retrieval system and its application in medical domain

2018 ◽  
Vol 7 (2.31) ◽  
pp. 181
Author(s):  
K Srinivasa Reddy ◽  
R Anandan ◽  
K Kalaivani ◽  
P Swaminathan
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Visual Information ◽  
Medical Images ◽  
Content Based Image Retrieval ◽  
Radiology Department ◽  
Query Image ◽  
Feature Extraction Method ◽  
Comprehensive Survey ◽  
Medical Domain

Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.  

Multi-Featured Content-Based Image Retrieval Using Color and Texture Features

2016 ◽  
Vol 850 ◽  
pp. 136-143 ◽  
Author(s):  
Mehmet Ayan ◽  
O. Ayhan Erdem ◽  
Hasan Şakir Bilge
Keyword(s):  
Feature Extraction ◽  
Image Retrieval ◽  
Texture Feature ◽  
Texture Features ◽  
Color Histogram ◽  
Superior Performance ◽  
Content Based Image Retrieval ◽  
Occurrence Matrix ◽  
Color Moment ◽  
Color And Texture Features

Content-based image retrieval (CBIR) system becomes a hot topic in recent years. CBIR system is the retrieval of images based on visual features. CBIR system based on a single feature has a low performance. Therefore, in this paper a new content based image retrieval method using color and texture features is proposed to improve performance. In this method color histogram and color moment are used for color feature extraction and grey level co-occurrence matrix (GLCM) is used for texture feature extraction. Then all extracted features are integrated for image retrieval. Finally, color histogram, color moment, GLCM and proposed methods are tested respectively. As a result, it is observed that proposed method which integrates color and texture features gave better results than the other methods used independently. To demonstrate the proposed system is successful, it was compared with existing CBIR systems. The proposed method showed superior performance than other comparative systems.

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.

Sign in / Sign up

Export Citation Format

Share Document