Integrating relevance feedback techniques for image retrieval using reinforcement learning

2005 ◽  
Vol 27 (10) ◽  
pp. 1536-1551 ◽  
Author(s):  
Peng-Yeng Yin ◽  
B. Bhanu ◽  
Kuang-Cheng Chang ◽  
Anlei Dong
Keyword(s):  
Reinforcement Learning ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Feedback Techniques

scholarly journals Interactive Content Based Image Retrieval using Multiuser Feedback

2017 ◽  
Vol 1 (4) ◽  
pp. 165
Author(s):  
M. Premkumar ◽  
R. Sowmya
Keyword(s):  
Image Retrieval ◽  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Human Perception ◽  
Content Based Image Retrieval ◽  
Rule Mining ◽  
Query Image ◽  
Feedback Techniques

Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

Performance Evaluation of Relevance Feedback for Image Retrieval by “Real-World” Multi-Tagged Image Datasets

2012 ◽  
Vol 3 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Roberto Tronci ◽  
Luca Piras ◽  
Giorgio Giacinto
Keyword(s):  
Performance Evaluation ◽  
Image Retrieval ◽  
Performance Measures ◽  
Real World ◽  
Relevance Feedback ◽  
Real Life ◽  
Content Based Image Retrieval ◽  
The Internet ◽  
Feedback Mechanisms ◽  
Feedback Techniques

Anyone who has ever tried to describe a picture in words is aware that it is not an easy task to find a word, a concept, or a category that characterizes it completely. Most images in real life represent more than a concept; therefore, it is natural that images available to users over the Internet (e.g., FLICKR) are associated with multiple tags. By the term ‘tag’, the authors refer to a concept represented in the image. The purpose of this paper is to evaluate the performances of relevance feedback techniques in content-based image retrieval scenarios with multi-tag datasets, as typically performances are assessed on single-tag dataset. Thus, the authors show how relevance feedback mechanisms are able to adapt the search to user’s needs either in the case an image is used as an example for retrieving images each bearing different concepts, or the sample image is used to retrieve images containing the same set of concepts. In this paper, the authors also propose two novel performance measures aimed at comparing the accuracy of retrieval results when an image is used as a prototype for a number of different concepts.

A Bayesian Image Retrieval Framework

2012 ◽  
pp. 143-159
Author(s):  
Rui Zhang ◽  
Ling Guan
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Visual Information ◽  
General Framework ◽  
Evaluation Criteria ◽  
Semantic Gap ◽  
Visual Content ◽  
Multiple Sources ◽  
Visual Appearance ◽  
Feedback Techniques

Conventional approaches to content-based image retrieval exploit low-level visual information to represent images and relevance feedback techniques to incorporate human knowledge into the retrieval process, which can only alleviate the semantic gap to some extent. To further boost the performance, a Bayesian framework is proposed in which information independent of the visual content of images is utilized and integrated with the visual information. Two particular instances of the general framework are studied. First, context which is the statistical relation across the images is integrated with visual content such that the framework can extract information from both the images and past retrieval results. Second, characteristic sounds made by different objects are utilized along with their visual appearance. Based on various performance evaluation criteria, the proposed framework is evaluated using two databases for the two examples, respectively. The results demonstrate the advantage of the integration of information from multiple sources.

Interaction Models and Relevance Feedback in Image Retrieval

2011 ◽  
pp. 160-186 ◽  
Author(s):  
Daniel Heesch ◽  
Stefan Ruger
Keyword(s):  
Human Computer Interaction ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Relevant Literature ◽  
Hierarchical Structures ◽  
Image Search ◽  
Comprehensive Review ◽  
Interaction Models ◽  
Retrieval Systems ◽  
Feedback Techniques

Human-computer interaction is increasingly recognised to be an indispensable component of image retrieval systems. A typical form of interaction is that of relevance feedback whereby users supply relevance information on the retrieved images. This information can subsequently be used to optimise retrieval parameters. The first part of the chapter provides a comprehensive review of existing relevance feedback techniques and also discusses a number of limitations that can be addressed more successfully in a browsing framework. Browsing models form the focus of the second part of this chapter where we will evaluate the merit of hierarchical structures and networks for interactive image search. This exposition aims to provide enough detail to enable the practitioner to implement many of the techniques and to find numerous pointers to the relevant literature otherwise.

Relevance feedback techniques in the MARS image retrieval system

2004 ◽  
Vol 9 (6) ◽  
pp. 535-547 ◽  
Author(s):  
Michael Ortega-Binderberger ◽  
Sharad Mehrotra
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Retrieval System ◽  
Image Retrieval System ◽  
Feedback Techniques
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