Interface for visualization of image database in adaptive image retrieval systems (AIRS)

2005 ◽  
Author(s):  
Anca Doloc-Mihu ◽  
Vijay V. Raghavan ◽  
Surendra Karnatapu ◽  
Chee-Hung H. Chu
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Retrieval Systems

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.

An Image Clustering and Feedback-based Retrieval Framework

2010 ◽  
Vol 1 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Chengcui Zhang ◽  
Liping Zhou ◽  
Wen Wan ◽  
Jeffrey Birch ◽  
Wei-Bang Chen
Keyword(s):  
Image Retrieval ◽  
Clustering Algorithm ◽  
Image Database ◽  
Image Clustering ◽  
Single Object ◽  
Image Region ◽  
Retrieval Accuracy ◽  
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.

WAVELET-BASED MULTIRESOLUTION HISTOGRAM FOR FAST IMAGE RETRIEVAL

2004 ◽  
Vol 02 (01) ◽  
pp. 59-73 ◽  
Author(s):  
PAWAN JAIN ◽  
S. N. MERCHANT
Keyword(s):  
Data Structure ◽  
Image Retrieval ◽  
Retrieval System ◽  
Performance Enhancement ◽  
Image Database ◽  
Retrieval Systems ◽  
Candidate Image ◽  
Image Retrieval System ◽  
Resolution Level ◽  
Structure Algorithm

Most of the content-based image retrieval systems require a distance computation of feature vectors for each candidate image in the image database. This exhaustive search is highly time-consuming and inefficient. This limits the usefulness of such system. Thus there is a growing need for a fast image retrieval system. Multiresolution data-structure algorithm provides a good solution to the above problem. In this paper we propose a wavelet-based multiresolution data-structure algorithm. Wavelet-based multiresolution data-structure further reduce the number of computation by around 50%. In the proposed approach we reuse the information obtained at lower resolution levels to calculate the distance at a higher resolution level. Apart from this, the proposed structure saves memory overheads by about 50% over multiresolution data-structure algorithm. The proposed algorithm can be easily combined with other algorithms for performance enhancement.4 In this paper we use the proposed technique to match luminance histogram for image retrieval. Fuzzy histograms enhances performance by considering the similarity between neighboring bins. We have extended the proposed approach to fuzzy histograms for better performance.

Approaches for Image Database Retrieval Based on Color, Texture, and Shape Features

2017 ◽  
pp. 28-50
Author(s):  
Kratika Arora ◽  
Ashwani Kumar Aggarwal
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Extraction Methods ◽  
Research Area ◽  
Content Based Image Retrieval ◽  
Good Precision ◽  
Retrieval Systems ◽  
Main Motive ◽  
Image Retrieval System ◽  
Active Research

With an ever-increasing use and demand for digital imagery in the areas of medicine, sciences, and engineering, image retrieval is an active research area in image processing and pattern recognition. Content-based image retrieval (CBIR) is a method of finding images from a huge image database according to persons' interests. Content-based here means that the search involves analysis of the actual content present in the image. As the database of images is growing day by day, researchers/scholars are searching for better techniques for retrieval of images with good efficiency.This chapter first gives an overview of the various image retrieval systems. Then, the applications of CBIR in various fields and existing CBIR systems are described. The various image content descriptors and extraction methods are also explained. The main motive of the chapter is to study and compare the features that are used in Content Based Image Retrieval system and conclude on the system that retrieves images from a huge database with good precision and recall.

Designing Image Retrieval System with the Concept of Visual Keys

2006 ◽  
Vol 10 (2) ◽  
pp. 136-144 ◽  
Author(s):  
Manabu Serata ◽  
◽  
Yutaka Hatakeyama ◽  
Kaoru Hirota
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
Text Retrieval ◽  
Image Feature ◽  
Feature Clustering ◽  
Retrieval Time ◽  
Retrieval Systems ◽  
Retrieval Efficiency ◽  
Image Retrieval System

A concept of visual keys is proposed to provide efficient and useful content-based image retrieval systems to users. Visual keys are defined as representative sub-images which are extracted from an image database by using image feature clustering. The proposed system is implemented and is tested on 1,000 images, which are included in the COREL database. Although the system makes use of only 80 sub-images from 8,962 ones extracted from the image database, the performance is kept with 90%. The retrieval time is within 4ms on the proposed system, which has retrieval efficiency like that of text retrieval by being applied text retrieval techniques, and thus the system is expected to provide the services on the WWW.

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