scholarly journals Building a latent semantic index of an image database from patterns of relevance feedback

2003 ◽  
Author(s):  
D.R. Heisterkamp
Keyword(s):  
Relevance Feedback ◽  
Image Database

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.

Image Retrieval Based on Contour and Relevance Feedback

2012 ◽  
Vol 182-183 ◽  
pp. 1771-1775
Author(s):  
Wei Lu
Keyword(s):  
Support Vector Machine ◽  
Relevance Feedback ◽  
Image Database ◽  
Support Vector ◽  
Moment Invariants ◽  
Query Image ◽  
Moment Invariant ◽  
Shift Rotation ◽  
Similar Images ◽  
Feedback Algorithm

In this paper an algorithm is proposed to retrieve images based on contour moment invariants of image and relevance feedback. Firstly, the contour of each query image is extracted and its contour moment invariant is computed. Then according to Euclid Distance between the query image and each image in the image database, the most similar images to the query image can be found. Finally, the relevance feedback algorithm based on support vector machine (SVM) is applied to improve retrieval precision. Experimental results show that the algorithm is more accurate and efficient to retrieve images with monotonous background and clear object and meet the invariance on shift, rotation and scale transform of objects.

A Two-Stage of Relevance Feedback for Content-Based Image Retrieval

2011 ◽  
Vol 467-469 ◽  
pp. 1627-1632
Author(s):  
Xue Feng Wang ◽  
Xing Su Chen
Keyword(s):  
Logistic Regression ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Image Database ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Target Image ◽  
Two Stage ◽  
Predictive Probabilities

In this paper, an effective relevance feedback (RF) approach is proposed in content-based image retrieval (CBIR). In the first stage, according to the user’s marked images, we get theirs predictive probabilities based-on Bayesian methodology which yields the posteriori of the images in the database; second via justify the weight of elements in each feature extracted of images, we refine features by logistic regression with positive features which get from the first stage. Then we rank the images according to the probability of the images in the database. The retrieval system is repeating until the user is satisfied with the feedback results or the target image has been found. Experimental results are shown to evaluate the method on a large image database in terms of precision and recall.

The Image Database Management System of Teaching File Using Personal Computer

1995 ◽  
Vol 32 (4) ◽  
pp. 677
Author(s):  
M J Shin ◽  
G W Kim ◽  
T J Chun ◽  
W H Ahn ◽  
S K Balk ◽  
...  
Keyword(s):  
Personal Computer ◽  
Management System ◽  
Database Management ◽  
Image Database ◽  
Database Management System ◽  
Teaching File
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