Relevance Feedback
Explore the VisLab research record for relevance feedback, including publications and project results preserved from the laboratory's legacy research pages.
| 8research records | 8visual summaries | 1research area |
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Image Retrieval with Feature Selection and Relevance FeedbackWe propose a new content based image retrieval (CBIR) system combined with relevance feedback and the online feature selection procedures. A measure of inconsistency from relevance feedback is explicitly used as a new semantic criterion to guide the feature selection. By integrating the user feedback information, the feature selection is able to bridge the gap between low-level visual features and high-level semantic information, leading to the improved image retrieval accuracy. Experimental results show that the proposed method obtains higher retrieval accuracy than a commonly used approach. |
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Long-Term Cross-Session Relevance Feedback Using Virtual FeaturesWe propose a novel RF framework, which facilitates the combination of short-term and long-term learning processes by integrating the traditional methods with a new technique called the virtual feature. The feedback history with all the users is digested by the system and is represented in a very efficient form as a virtual feature of the images. By monitoring the changes in retrieval performance, the proposed system can automatically adapt the concepts according to the new subject concepts. The results manifest that the proposed framework outperforms the traditional within-session and log-based long-term RF techniques. |
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Integrating Relevance Feedback Techniques for Image RetrievalWe propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques in a content-based image retrieval system. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone. Further, the storage demand is significantly reduced by the concept digesting technique. |
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Reinforcement learning for combining relevance feedback techniques in image retrievalRelevance feedback (RF) is an interactive process which refines the retrievals by utilizing user’s feedback history. Most researchers strive to develop new RF techniques and ignore the advantages of existing ones. In this paper, we propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. Also, a concept digesting method is proposed to reduce the complexity of storage demand. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone, and that the sharing of relevance knowledge between multiple query sessions also provides significant contributions for improvement. Further, the storage demand is significantly reduced by the concept digesting technique. This shows the scalability of the proposed model against a growing-size database. |
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Improving retrieval performance by long-term relevance informationRelevance feedback (RF) is an iterative process which improves the retrieval performance by utilizing the user’s feedback on retrieved results. Traditional RF techniques uses solely the short-term experience and are short of knowledge of cross-session agreement. In this paper, we propose a novel RF framework which facilitates the combination of short-term and long-term experiences by integrating the traditional methods and a new technique called the virtual feature. The feedback history of all the users is digested by the system and is represented as a virtual feature of the images. As such, the dissimilarity measure can be adapted dynamically depending on the estimate of the relevance probability derived from the virtual features. The results manifest that the proposed framework outperforms the one that adopts a single traditional RF technique. |
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Concept learning with fuzzy clustering and relevance feedbackIn recent years feedback approaches have been used in relating low-level image features with concepts to overcome the subjective nature of the human image interpretation. Generally, in these systems when the user starts with a new query, the entire prior experience of the system is lost. In this paper, we address the problem of incorporating prior experience of the retrieval system to improve the performance on future queries. We propose a semi-supervised fuzzyclustering method to learn class distribution (meta knowledge) in the sense of high-level concepts from retrieval experience. Using fuzzyrules, we incorporate the meta knowledge into a probabilistic feature relevance feedback approach to improve the retrieval performance. Results on synthetic and real databases show that our approach provides better retrieval precision compared to the case when no retrieval experience is used. |
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Probabilistic Feature Relevance Learning for Content-Based Image RetrievalMost of the current image retrieval systems use “one-shot” queries to a database to retrieve similar images. Typically a K -nearest neighbor kind of algorithm is used, where weights measuring feature importance along each input dimension remain fixed (or manually tweaked by the user), in the computation of a given similarity metric. In this paper, we present a novel probabilistic method that enables image retrieval procedures to automatically capture feature relevance based on user's feedback and that is highly adaptive to query locations. Experimental results are presented that demonstrate the efficacy of our technique using both simulated and real-world data. |
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Probabilistic feature relevance learning for online indexingIn this paper, we present a novel probabilistic method that enables image retrieval procedures to automatically capture feature relevance based on user’s feedback and that is highly adaptive to query locations. The method estimates the strength of each feature for predicting the query using feedback provided by the user, thereby capturing relevance measures for online indexing. Experimental results are presented that demonstrate the efficacy of our technique using both simulated and real-world data. |