IR_URFS_VF: image recommendation with user relevance feedback session and visual features in vertical image search
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Recently,the relevance feedback technique has been one of the important research facts in CBIR.Because it has greatly reduced the gap between the high level notion and low level visual features,the retrieval results are better,because of its versatility and splendid classified ability,SVM are introduced gradually in the image retrieval system.To further raise the retrieval efficiency,use the third-level feedback mechanism introducing fuzzy relevance,users mark the result for the related image,the fuzzy related image and the non-correlated image,and revise the inquiry vector migration algorithm,based on this utilize multi-classification SVM to propose one new relevance feedback image retrieval method.Through experiments can see this is an effective method,raising the image retrieval efficiency.
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Content-based Image Retrieval (CBIR) using relevance feedback technique is applied to improve the results of traditional techniques in image retrieva l. Since the results returned by system cannot full y satisfy users and the iteration process of feedback can be very time-consuming and tedious, log-based relevance feedback is introduce to the system. In previous wo rk, we have already introduced multi-level log-base d relevance feedback scheme for image retrieval to ac celerate the iteration process and to increase the hit rate. In this paper, we improve the novel algorithm and apply it in a demo image retrieval system whic h presents refined results based on multi-level log-b ased relevance feedback for Content-based Image Retrieval.
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One’s writing originality in academic world becomes more and more questionable
along with the increasingly access to others’ writings due to files archiving technology
development today, especially over the internet. Therefore, a text similarity detection
system is required. Based on that problem, this research tries to provide the solution by
developing an application with the concept of text mining which implements cosine
similarity and Smith-Waterman algorithm to detect text similarity. Cosine similarity
serves to measure text similarity based on words occurrence, while Smith-Waterman
algorithm’s function is to calculate text similarity based on words sequence. Based on
this research test result, the developed application successfully detects text similarity
from very similar to very dissimilar pair of texts.
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Region-based image retrieval (RBIR), a special type of content based image retrieval (CBIR), is an important research. This paper presents integration of RBIR with relevance feedback (RF) to enhance the performance of CBIR. Watershed algorithm is used to extract regions but not all regions are with the same importance. So, a region-weighting scheme reflecting the process of human visual perception is proposed. By using relevance feedback method, the matching process could improve retrieval performance interactively and allow progressive refinement of query results according to the user's feedback action.
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This paper proposes a new, fast approach for relevance feedback in content-based image retrieval systems. The main advantage of the proposed approach is the use of the set of primarily retrieved images instead of performing another query. The images are hierarchically clustered with respect to the positive/ negative examples provided by the user, in a continuous manner, as the user successively browses through new sets of retrieved images. The proposed aggregative hierarchical clustering relevance feedback embeds an automatic, adaptive stopping criterion. The paper further investigates the effect of the inter-cluster dissimilarity metric (minimum distance, maximum distance, centroid distance and medium distance) on the image retrieval performance for various image databases.
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Due to the recent improvements in digital photography and storage capacity, storing large amounts of images has been made possible, and efficient means to retrieve images matching a user's query are needed. Content-based Image Retrieval (CBIR) systems automatically extract image contents based on image features, i.e. color, texture, and shape. Relevance feedback methods are applied to CBIR to integrate users' perceptions and reduce the gap between high-level image semantics and low-level image features. In the past 30 years, relevance feedback (RF) has been an effective query modification approach to improving the performance of information retrieval (IR) by interactively asking a user whether a set of documents are relevant or not to a given query concept. This paper aims at developing a scheme for intelligent image retrieval using machine learning technique and the information gathered from the user's feedback. This helps the system on the following rounds of the retrieval process to better approximate the present need of the user. We have shown that a powerful relevance feedback mechanism can be implemented by using reinforcement learning algorithms. The user thus does not need to explicitly specify weights for relationship between images and concepts, because the weights are formed implicitly by the system. The proposed relevance feedback technique is described, analyzed qualitatively, and visualized in the paper. Also, its performance is compared with a reference method. Experimental results demonstrate that our proposed technique is promising.
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Relevance feedback (RF) has been an active research area in content-based image retrieval (CBIR). RF intends to bridge the gap between the low-level image features and the high-level human visual perception by analyzing and employing the feedback information provided by the user. This gap becomes more evident and important in medical image retrieval due to the two distinct facts with regard to medical images: (1) subtle differences between images, even between pathological and non-pathological images; (2) subjective and different diagnosis even among experts. This paper describes a novel linear weight-updating approach for RF applying to spine X-ray image retrieval. The algorithm utilizes both positive and negative examples to gain feedback from the user. Experimental results show that the proposed approach can substantially improve the retrieval performance to better satisfy the individual user's preferences.
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The present paper describes a query-by-sketch image retrieval system aimed at reducing the semantic gap by adopting relevance feedback. To reduce the semantic gap between low-level visual features and high-level semantics, in this content-based image retrieval system, users' sketches play an important role in relevance feedback. When users mark similar images of output images with "relevant" labels, the "relevant" images are relevant to the sketch image in positive feedback. This method was applied to 5,500 images in Corel Photo Gallery. Experimental results show that the proposed method is effective in retrieving images.
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New relevance feedback algorithms have been developed for content-based image retrieval (CBIR) that allow the user to achieve more flexible query. In conjunction with the new user interface, called group-oriented user interface, the user's interest can be expressed with multiple groups of positive and negative image examples. This provides users with greater flexibility as compared with previous systems that consider image query as one or two-class problems. In this paper, we analyze our new algorithm qualitatively and quantitatively. For comparison with previous approaches, the systems are tested on both toy problems and real image retrieval tasks. From the results of our experiments, we suggest when and how our algorithm has advantages over the previous methods.
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