Browsing by Subject "Relevance feedback"
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Item Open Access Diverse relevance feedback for time series with autoencoder based summarizations(IEEE Computer Society, 2018) Eravci, B.; Ferhatosmanoglu, H.We present a relevance feedback based browsing methodology using different representations for time series data. The outperforming representation type, e.g., among dual-tree complex wavelet transformation, Fourier, symbolic aggregate approximation (SAX), is learned based on user annotations of the presented query results with representation feedback. We present the use of autoencoder type neural networks to summarize time series or its representations into sparse vectors, which serves as another representation learned from the data. Experiments on 85 real data sets confirm that diversity in the result set increases precision, representation feedback incorporates item diversity and helps to identify the appropriate representation. The results also illustrate that the autoencoders can enhance the base representations, and achieve comparably accurate results with reduced data sizes.Item Open Access Diversity based Relevance Feedback for Time Series Search(2013) Eravci, B.; Ferhatosmanoglu H.We propose a diversity based relevance feedback approach for time series data to improve the accuracy of search results. We first develop the concept of relevance feedback for time series based on dual-tree complex wavelet (CWT) and SAX based approaches. We aim to enhance the search quality by incorporating diversity in the results presented to the user for feedback. We then propose a method which utilizes the representation type as part of the feedback, as opposed to a human choosing based on a preprocessing or training phase. The proposed methods utilize a weighting to handle the relevance feedback of important properties for both single and multiple representation cases. Our experiments on a large variety of time series data sets show that the proposed diversity based relevance feedback improves the retrieval performance. Results confirm that representation feedback incorporates item diversity implicitly and achieves good performance even when using simple nearest neighbor as the retrieval method. To the best of our knowledge, this is the first study on diversification of time series search to improve retrieval accuracy and representation feedback. © 2013 VLDB Endowment.Item Open Access Elicitation and use of relevance feedback information(Elsevier Ltd, 2006-01) Vechtomova, O.; Karamuftuoglu, M.The paper presents two approaches to interactively refining user search formulations and their evaluation in the new High Accuracy Retrieval from Documents (HARD) track of TREC-12. The first method consists of asking the user to select a number of sentences that represent documents. The second method consists of showing to the user a list of noun phrases extracted from the initial document set. Both methods then expand the query based on the user feedback. The TREC results show that one of the methods is an effective means of interactive query expansion and yields significant performance improvements. The paper presents a comparison of the methods and detailed analysis of the evaluation results. © 2004 Elsevier Ltd. All rights reserved.Item Open Access Query expansion with terms selected using lexical cohesion analysis of documents(Elsevier Ltd, 2007-07) Vechtomova, O.; Karamuftuoglu, M.We present new methods of query expansion using terms that form lexical cohesive links between the contexts of distinct query terms in documents (i.e., words surrounding the query terms in text). The link-forming terms (link-terms) and short snippets of text surrounding them are evaluated in both interactive and automatic query expansion (QE). We explore the effectiveness of snippets in providing context in interactive query expansion, compare query expansion from snippets vs. whole documents, and query expansion following snippet selection vs. full document relevance judgements. The evaluation, conducted on the HARD track data of TREC 2005, suggests that there are considerable advantages in using link-terms and their surrounding short text snippets in QE compared to terms selected from full-texts of documents. © 2006 Elsevier Ltd. All rights reserved.Item Open Access A relevance feedback technique for multimodal retrieval of news videos(IEEE, 2005-11) Aksoy, Selim; Çavuş ÖzgeContent-based retrieval in news video databases has become an important task with the availability of large quantities of data in both public and proprietary archives. We describe a relevance feedback technique that captures the significance of different features at different spatial locations in an image. Spatial content is modeled by partitioning images into non-overlapping grid cells. Contributions of different features at different locations are modeled using weights defined for each feature in each grid cell. These weights are iteratively updated based on user's feedback in terms of positive and negative labeling of retrieval results. Given this labeling, the weight updating scheme uses the ratios of standard deviations of the distances between relevant and irrelevant images to the standard deviations of the distances between relevant images. The proposed technique is quantitatively and qualitatively evaluated using shots related to several sports from the news video collection of the TRECVID video retrieval evaluation where the weights could capture relative contributions of different features and spatial locations. © 2005 IEEE.Item Open Access Semantic scene classification for content-based image retrieval(Bilkent University, 2008) Çavuş, ÖzgeContent-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. Because of the constantly increasing complexity of the image content, low-level features are no longer sufficient for image content representation. In this study, a content-based image retrieval framework that is based on scene classification for image indexing is proposed. First, the images are segmented into regions by using their color and line structure information. By using the line structures of the images the regions that do not consist of uniform colors such as man made structures are captured. After all regions are clustered, each image is represented with the histogram of the region types it contains. Both multi-class and one-class classification models are used with these histograms to obtain the probability of observing different semantic classes in each image. Since a single class with the highest probability is not sufficient to model image content in an unconstrained data set with a large number of semantically overlapping classes, the obtained probability values are used as a new representation of the images and retrieval is performed on these new representations. In order to minimize the semantic gap, a relevance feedback approach that is based on the support vector data description is also incorporated. Experiments are performed on both Corel and TRECVID datasets and successful results are obtained.Item Open Access Sparsity Based Image Retrieval using relevance feedback(IEEE, 2012) Günay, Osman; Çetin, A. EnisIn this paper, a Content Based Image Retrieval (CBIR) algorithm employing relevance feedback is developed. After each round of user feedback Biased Discriminant Analysis (BDA) is utilized to find a transformation that best separates the positive samples from negative samples. The algorithm determines a sparse set of eigenvectors by L1 based optimization of the generalized eigenvalue problem arising in BDA for each feedback round. In this way, a transformation matrix is constructed using the sparse set of eigenvectors and a new feature space is formed by projecting the current features using the transformation matrix. Transformations developed using the sparse signal processing method provide better CBIR results and computational efficiency. Experimental results are presented. © 2012 IEEE.