Content Based Image Retrieval Model in an Object Oriented Database

1998 ◽  
pp. 263-275
Author(s):  
Chabane Djeraba ◽  
Ian Savory ◽  
Max Barere ◽  
Steve Marchand
Keyword(s):  
Image Retrieval ◽  
Object Oriented ◽  
Content Based Image Retrieval ◽  
Retrieval Model ◽  
Object Oriented Database

A Content Based Image Retrieval Model Using Feature Space Dividing

2014 ◽  
Vol 596 ◽  
pp. 388-393
Author(s):  
Guan Huang
Keyword(s):  
Image Retrieval ◽  
Fractional Order ◽  
Feature Space ◽  
Image Feature ◽  
Content Based Image Retrieval ◽  
Time Cost ◽  
Retrieval Model ◽  
Matching Accuracy ◽  
Query Image ◽  
Proposed Model

This paper introduces a model for content based image retrieval. The proposed model extracts image color, texture and shape as feature vectors; and then the image feature space is divided into a group of search zones; during the image searching phase, the fractional order distance is utilized to evaluate the similarity between images. As the query image vector only needs to compare with library image vectors located in the same search zone, the time cost is largely reduced. Further more the fractional order distance is utilized to improve the vector matching accuracy. The experimental results demonstrated that the proposed model provides more accurate retrieval results with less time cost compared with other methods.

scholarly journals A New Content Based Image Retrieval Model Based on Wavelet Transform

2015 ◽  
Vol 03 (03) ◽  
pp. 66-73 ◽  
Author(s):  
Davar Giveki ◽  
Ali Soltanshahi ◽  
Fatemeh Shiri ◽  
Hadis Tarrah
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Retrieval Model ◽  
Model Based

A Content-Based Approach to Medical Image Retrieval

2021 ◽  
pp. 114-136
Author(s):  
Anitha K. ◽  
Naresh K. ◽  
Rukmani Devi D.
Keyword(s):  
Neural Networks ◽  
Image Retrieval ◽  
System Performance ◽  
Medical Images ◽  
Direct Method ◽  
Local Binary Patterns ◽  
Content Based Image Retrieval ◽  
Retrieval Model ◽  
Retrieval Time ◽  
Similar Images

Medical images stored in distributed and centralized servers are referred to for knowledge, teaching, information, and diagnosis. Content-based image retrieval (CBIR) is used to locate images in vast databases. Images are indexed and retrieved with a set of features. The CBIR model on receipt of query extracts same set of features of query, matches with indexed features index, and retrieves similar images from database. Thus, the system performance mainly depends on the features adopted for indexing. Features selected must require lesser storage, retrieval time, cost of retrieval model, and must support different classifier algorithms. Feature set adopted should support to improve the performance of the system. The chapter briefs on the strength of local binary patterns (LBP) and its variants for indexing medical images. Efficacy of the LBP is verified using medical images from OASIS. The results presented in the chapter are obtained by direct method without the aid of any classification techniques like SVM, neural networks, etc. The results prove good prospects of LBP and its variants.

scholarly journals A Content Based Image Retrieval Model for E-Commerce

2018 ◽  
Vol 7 (``11) ◽  
pp. 24392-24396
Author(s):  
Gibson Kimutai ◽  
Prof. Wilson Cheruiyot ◽  
Dr. Calvins Otieno
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Retrieval Model ◽  
Large Database ◽  
Visual Content ◽  
Color Feature ◽  
Search Results ◽  
Efficient Retrieval ◽  
Database Content

In the last decade, large database of images have grown rapidly. This trend is expected to continue in to the future. Retrieval and querying of these image in efficient way is a challenge in order to access the visual content from large database. Content Based Image Retrieval (CBIR) provides the solution for efficient retrieval of image from these huge image database. Many research efforts have been directed to this area with color feature being the mostly used feature because of its ease of extraction. Although many research efforts have been directed to this area, precision  of majority of the developed models  are still at less than 80%. This is a challenge as it leads to unsatisfying search results. This paper proposes a Content Based Image Retrieval model for E-Commerce.

Sign in / Sign up

Export Citation Format

Share Document