Content Based Image Retrieval using Relevance Feedback

2006

Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques were designed around query refinement based on relevance feedback, suffer from slow convergence, and do not even guarantee to find intended targets. To address those limitations, we propose several efficient query point movement methods. We theoretically prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be employed to improve the effectiveness and efficiency of existing CBIR systems.

2013

Content Based Image Retrieval (CBIR) systems attempt to allow users to perform searches in large image repositories. Content-Based Image Retrieval (CBIR) has become one of the most progressive research areas in the past few years. In content Based Image Retrieval, images are retrieved based on color, texture and shape (low level perception). There is a gap between user semantics (high level perception/concepts) and low level perception is called ‘Semantic Gap’. Relevance Feedback (Relevance Feedback) learns association between high level semantics and low level features. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems are semantic gap and human perception of visual content respectively. In this paper, we propose different aspects of the system such as first, we analyze the nature of the Relevance Feedback problem in a continuous representation space in the context of image retrieval. Secondly, a Relevance Feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's feedback and finally, the proposed system where user can view/understands the relevance level of the retrieved result of images to his/her given query image. The proposed approach greatly reduces the user's effort of composing a query and captures the user's information need more specifically. We can reduce the user intervention in the CBIR retrieval system.

2000

Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. In content-based image retrieval it is more and more frequently used and very good results have been obtained. However, too much negative feedback may destroy a query as good features get negative weightings.

Content-based image retrieval (CBIR) is the basis of image retrieval systems. Unlike traditional database queries, content-based multimedia retrieval queries are imprecise in nature which makes it difficult for users to express their exact information need in the form of a precise right query. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. A novel method, Navigation-Pattern-based Relevance Feedback (NPRF), is used to achieve the high efficiency and effectiveness of CBIR with the large-scale image data. In terms of efficiency, the iterations of feedback are reduced by using the navigation patterns discovered from the user query log. In terms of effectiveness, the proposed search algorithm NPRFSearch makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks.

International Journal of Computer and Electrical Engineering, 2010

In this paper, a new system of fuzzy relevance feedback for image retrieval is introduced. In conventional CBIR systems, the users are restricted to make a binary labeling on the retrieval results, while this determination is difficult for rich images in semantic. In the proposed system, we accumulate user interactions using a soft feedback model to construct Fuzzy Transaction Repository (FTR). The repository remembers the user's intent and, therefore, in terms of the semantic meanings, provides a better representation of each image in the database. To best exploit the benefits of user feedback, we improved the proposed system, so that the repository remembers the user's intent in a suitable manner (as structure-based fuzzy transaction repository) and provides an accurate representation for each image in the database. The semantic similarity between the query and each database image can then be computed using the current feedback and the semantic values in the FTR. Furthermore, feature re-weighting is applied to the session-term feedback in order to learn the weight of low-level features. These two similarity measures are normalized and combined together to form the overall similarity measure. Our experimental results show that the average precision of the proposed systems exceeds 83% after three iterations.

International Journal

Image retrieval based on image content has become a hot topic in the field of image processing and computer vision. Contentbased image retrieval (CBIR) is the basis of image retrieval systems. Unlike traditional database queries, content-based multimedia retrieval queries are imprecise in nature which makes it difficult for users to express their exact information need in the form of a precise right query. To be more profitable, relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. However, existing relevance feedback based CBIR methods usually request a number of iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and inefficient in real applications. This paper studies about the research on ways to extend and improve query methods for image databases is widespread, we have developed the QBIC (Query by Image Content) system to explore content-based retrieval methods. To achieve the high efficiency and effectiveness of CBIR we are using two type of methods for feature extraction like SVM (support vector machine)and NPRF(navigation-pattern based relevance feedback). By using SVM classifier as a category predictor of query and database images, they are exploited at first to filter out irrelevant images by its different low-level, concept and key point-based features. Thus we may reduce the size of query search in the data base then we may apply NPRF algorithm and refinement strategies for further extraction. In terms of efficiency, the iterations of feedback are reduced by using the navigation patterns discovered from the user query log. In terms of effectiveness, the proposed search algorithm NPRF Search makes use of the discovered navigation patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query Expansion (QEX), to converge the search space toward the user's intention effectively. By using NPRF method, high quality of image retrieval on RF can be achieved in a small number of feedbacks.

1997 Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries, 1997

Content-based multimedia information retrieval MIR has become one of the most active research areas in the past few years. Many retrieval approaches based on extracting and representing visual properties of multimedia data have been developed. While these approaches establish the viability of MIR based on visual features, techniques for incorporating human expertise directly during the query process to improve retrieval performance have not drawn enough attention. To address this limitation, this paper introduces a Human-Computer Interaction based approach to MIR in which the user guides the system during retrieval using relevance feedback. Our experiments show that the retrieval performance improves signi cantly by incorporating humans in the retrieval process.

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/relevance-feedback-in-content-based-image-retrieval https://www.ijert.org/research/relevance-feedback-in-content-based-image-retrieval-IJERTV3IS20760.pdf CBIR has been a very active research area .CBIR is the mainstay of current image retrieval system. The purpose of CBIR is to present an image conceptually, with a set of low-level visual features such as color, texture, and shape. The computational complexity and retrieval accuracy are main problems in CBIR. To avoid this problem, this paper provides a overview on a new content-based image retrieval method using color and texture feature with relevance feedback. Relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. Relevance feedback is a powerful technique in CBIR systems. However, existing relevance feedback-based CBIR methods usually request a number of iterative feedbacks. So our system will try to reduce the number of feedback by mining the navigation behavior of user. That navigation behavior will be stored in the log database.

Relevance feedback is effective technique for bridging the semantic gap in image retrieval which diminish semantic gap between low-level visual features and high-level semantic concepts for image retrieval. Currently, crucial image retrieval system is content-based image retrieval. To improve performance of proposed content based image retrieval system, automatic relevance feedback technique is proposed which based on inductive learning involve inductive concept learning by decision tree. We implement and tested proposed image retrieval system which refine the retrieval result as per user requirement until get required accuracy. Experimental result shows improved performance in term of precision, recall and accuracy.

2007

In this paper, a new method for image retrieval is proposed. The method combines three existing methods: queryby-example, browsing, and relevance feedback. The method assists users in finding images of the same scene or genre in image databases. First, the user can express query by presenting an example image. Then, the user is given the opportunity to browse through the images, and to refine the initial query in additional relevance feedback loops. The browsing environment consists of a similarity pyramid, in which the different layers represent the database with varying levels of detail. In this way, users can smoothly browse across the images in the database and focus on the relevant ones.