Image retrieval systems based on compact shape descriptor and relevance feedback information

2011 ◽  
Vol 22 (5) ◽  
pp. 378-390 ◽  
Author(s):  
Konstantinos Zagoris ◽  
Kavallieratou Ergina ◽  
Nikos Papamarkos
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Shape Descriptor ◽  
Feedback Information ◽  
Retrieval Systems ◽  
Compact Shape

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.

scholarly journals ACCURATE IMAGE RETRIEVAL BASED ON COMPACT COMPOSITE DESCRIPTORS AND RELEVANCE FEEDBACK INFORMATION

2010 ◽  
Vol 24 (02) ◽  
pp. 207-244 ◽  
Author(s):  
SAVVAS A. CHATZICHRISTOFIS ◽  
KONSTANTINOS ZAGORIS ◽  
YIANNIS S. BOUTALIS ◽  
NIKOS PAPAMARKOS
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Databases ◽  
User Preferences ◽  
Image Information ◽  
Feedback Information ◽  
Indexing And Retrieval ◽  
Image Retrieval System ◽  
Image Indexing And Retrieval ◽  
Large Image Databases

In this paper a new set of descriptors appropriate for image indexing and retrieval is proposed. The proposed descriptors address the tremendously increased need for efficient content-based image retrieval (CBIR) in many application areas such as the Internet, biomedicine, commerce and education. These applications commonly store image information in large image databases where the image information cannot be accessed or used unless the database is organized to allow efficient storage, browsing and retrieval. To be applicable in the design of large image databases, the proposed descriptors are compact, with the smallest requiring only 23 bytes per image. The proposed descriptors' structure combines color and texture information which are extracted using fuzzy approaches. To evaluate the performance of the proposed descriptors, the objective Average Normalized Modified Retrieval Rank (ANMRR) is used. Experiments conducted on five benchmarking image databases demonstrate the effectiveness of the proposed descriptors in outperforming other state-of-the-art descriptors. Also, a Auto Relevance Feedback (ARF) technique is introduced which is based on the proposed descriptors. This technique readjusts the initial retrieval results based on user preferences improving the retrieval score significantly. An online demo of the image retrieval system img(Anaktisi) that implements the proposed descriptors can be found at .

scholarly journals Multimodal Markov Random Field for Image Reranking Based on Relevance Feedback

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Ricardo Omar Chávez ◽  
Hugo Jair Escalante ◽  
Manuel Montes-y-Gómez ◽  
Luis Enrique Sucar
Keyword(s):  
Image Retrieval ◽  
Random Field ◽  
Markov Random Field ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Experimental Results ◽  
Feedback Information ◽  
Markov Random ◽  
Initial Retrieval ◽  
Image Reranking

This paper introduces a multimodal approach for reranking of image retrieval results based on relevance feedback. We consider the problem of reordering the ranked list of images returned by an image retrieval system, in such a way that relevant images to a query are moved to the first positions of the list. We propose a Markov random field (MRF) model that aims at classifying the images in the initial retrieval-result list as relevant or irrelevant; the output of the MRF is used to generate a new list of ranked images. The MRF takes into account (1) the rank information provided by the initial retrieval system, (2) similarities among images in the list, and (3) relevance feedback information. Hence, the problem of image reranking is reduced to that of minimizing an energy function that represents a trade-off between image relevance and interimage similarity. The proposed MRF is a multimodal as it can take advantage of both visual and textual information by which images are described with. We report experimental results in the IAPR TC12 collection using visual and textual features to represent images. Experimental results show that our method is able to improve the ranking provided by the base retrieval system. Also, the multimodal MRF outperforms unimodal (i.e., either text-based or image-based) MRFs that we have developed in previous work. Furthermore, the proposed MRF outperforms baseline multimodal methods that combine information from unimodal MRFs.

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