A Query Model with Relevance Feedback for Image Database Retrieval

2006 ◽  
Author(s):  
Sahudy Montenegro Gonzalez ◽  
Akebo Yamakami
Keyword(s):  
Relevance Feedback ◽  
Image Database ◽  
Database Retrieval ◽  
Query Model

Image database retrieval utilizing affinity relationships

2003 ◽  
Author(s):  
Mei-Ling Shyu ◽  
Shu-Ching Chen ◽  
Min Chen ◽  
Chengcui Zhang ◽  
Kanoksri Sarinnapakorn
Keyword(s):  
Image Database ◽  
Database Retrieval

An Image Clustering and Feedback-Based Retrieval Framework

2012 ◽  
pp. 62-80
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Image Region ◽  
Retrieval Systems ◽  
Object Based ◽  
Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

Simulation of Fast Retrieval Method for Large-Scale Image Database

2014 ◽  
Vol 556-562 ◽  
pp. 4959-4962
Author(s):  
Sai Qiao
Keyword(s):  
Feature Extraction ◽  
Large Scale ◽  
Great Increase ◽  
Texture Features ◽  
Image Database ◽  
Image Databases ◽  
Matching Method ◽  
Retrieval Method ◽  
Database Retrieval ◽  
Database Constraints

The traditional database information retrieval method is achieved by retrieving simple corresponding association of the attributes, which has the necessary requirement that image only have a single characteristic, with increasing complexity of image, it is difficult to process further feature extraction for the image, resulting in great increase of time consumed by large-scale image database retrieval. A fast retrieval method for large-scale image databases is proposed. Texture features are extracted in the database to support retrieval in database. Constraints matching method is introduced, in large-scale image database, referring to the texture features of image in the database to complete the target retrieval. The experimental results show that the proposed algorithm applied in the large-scale image database retrieval, augments retrieval speed, thereby improves the performance of large-scale image database.

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