Search results for "image retrieval"

showing 10 items of 69 documents

A P2P Architecture for Multimedia Content Retrieval

2006

The retrieval facilities of most Peer-to-Peer (P2P) systems are limited to queries based on unique identifiers or small sets of keywords. This approach can be highly labor-intensive and inconsistent. In this paper we investigate a scenario where a huge amount of multimedia resources are shared in a P2P network, by means of efficient content-based image and video retrieval functionalities. The challenge in such systems is to limit the number of sent messages, maximizing the usefulness of each peer contacted in the query process. We achieve this goal by the adoption of a novel algorithm for routing user queries. The proposed approach exploits compact representations of multimedia resources sh…

Information retrievalMultimediaComputer scienceOverlay networkUser interfacecomputer.software_genreNetwork topologyImage retrievalcomputerShared resource
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A NSGA Based Approach for Content Based Image Retrieval

2013

The purpose of CBIR Content Based Image Retrieval systems is to allow users to retrieve pictures related to a semantic concept of their interest, when no other information but the images themselves is available. Commonly, a series of images are presented to the user, who judges on their relevance. Several different models have been proposed to help the construction of interactive systems based on relevance feedback. Some of these models consider that an optimal query point exists, and focus on adapting the similarity measure and moving the query point so that it appears close to the relevant results and far from those which are non-relevant. This implies a strong causality between the low l…

Information retrievalOptimization problemPoint of interestRelevance feedbackRelevance (information retrieval)Data miningSimilarity measureContent-based image retrievalFocus (optics)computer.software_genreImage retrievalcomputerMathematics
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Distributed image retrieval on DAISY

2006

The paper describes an application of image retrieval based on DAISY architecture (distributed architecture for intelligent system). The creation of pictorial indexes may require a number of hours depending on the size of the pictorial data base. The problem can become more complex in the case of distributed database systems. In both cases a distributed architecture can be the natural and more efficient solution. DAISY architecture is based on the concept of co-operating behavioral agents supervised by a central engagement module. Preliminary experiments, to evaluate the performance of the system, have been performed on a astronomical database and coral image

Information retrievalSettore INF/01 - InformaticaDistributed databaseComputer scienceDistributed database management systemsMulti-agent systemArchitectureDistributed systems image retrievalBase (topology)Image retrievalImage (mathematics)Database index2003 IEEE International Workshop on Computer Architectures for Machine Perception
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An interactive evolutionary approach for content based image retrieval

2009

Content Based Image Retrieval (CBIR) systems aim to provide a means to find pictures in large repositories without using any other information except its contents usually as low-level descriptors. Since these descriptors do not exactly match the high level semantics of the image, assessing perceptual similarity between two pictures using only their feature vectors is not a trivial task. In fact, the ability of a system to induce high level semantic concepts from the feature vector of an image is one of the aspects which most influences its performance. This paper describes a CBIR algorithm which combines relevance feedback, evolutionary computation concepts and ad-hoc strategies in an attem…

Information retrievalbusiness.industryComputer scienceFeature vectorFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONRelevance feedbackPattern recognitionContent-based image retrievalSemanticsEvolutionary computationHistogramVisual WordArtificial intelligencebusinessImage retrieval2009 IEEE International Conference on Systems, Man and Cybernetics
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Multimedia Retrieval by Means of Merge of Results from Textual and Content Based Retrieval Subsystems

2010

The main goal of this paper it is to present our experiments in ImageCLEF 2009 Campaign (photo retrieval task). In 2008 we proved empirically that the Text-based Image Retrieval (TBIR) methods defeats the Content-based Image Retrieval CBIR "quality" of results, so this time we developed several experiments in which the CBIR helps the TBIR. The TBIR System [6] main improvement is the named-entity sub-module. In case of the CBIR system [3] the number of low-level features has been increased from the 68 component used at ImageCLEF 2008 up to 114 components, and only the Mahalanobis distance has been used. We propose an ad-hoc management of the topics delivered, and the generation of XML struct…

InformáticaMahalanobis distanceTelecomunicacionesInformation retrievalcomputer.internet_protocolComputer scienceSearch engine indexing02 engineering and technologyContent-based image retrieval01 natural sciencesData retrievalHuman–computer information retrieval0103 physical sciences0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingVisual Word010306 general physicsImage retrievalcomputerXML
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Some Results Using Different Approaches to Merge Visual and Text-Based Features in CLEF’08 Photo Collection

2009

This paper describes the participation of the MIRACLE team at the ImageCLEF Photographic Retrieval task of CLEF 2008. We succeeded in submitting 41 runs. Obtained results from text-based retrieval are better than content-based as previous experiments in the MIRACLE team campaigns [5, 6] using different software. Our main aim was to experiment with several merging approaches to fuse text-based retrieval and content-based retrieval results, and it happened that we improve the text-based baseline when applying one of the three merging algorithms, although visual results are lower than textual ones.

InformáticaTelecomunicacionesInformation retrievalComputer sciencebusiness.industrySearch engine indexingInformationSystems_INFORMATIONSTORAGEANDRETRIEVAL020206 networking & telecommunications02 engineering and technologyClefSoftwareHuman–computer information retrieval0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingVisual WordDocument retrievalbusinessImage retrievalMerge (version control)
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A relevance feedback CBIR algorithm based on fuzzy sets

2008

CBIR (content-based image retrieval) systems attempt to allow users to perform searches in large picture repositories. In most existing CBIR systems, images are represented by vectors of low level features. Searches in these systems are usually based on distance measurements defined in terms of weighted combinations of the low level features. This paper presents a novel approach to combining features when using multi-image queries consisting of positive and negative selections. A fuzzy set is defined so that the degree of membership of each image in the repository to this fuzzy set is related to the user's interest in that image. Positive and negative selections are then used to determine t…

Iterative methodbusiness.industryFuzzy setRelevance feedbackUsabilityMachine learningcomputer.software_genreImage (mathematics)Set (abstract data type)Signal ProcessingComputer Vision and Pattern RecognitionArtificial intelligenceElectrical and Electronic EngineeringbusinessImage retrievalAlgorithmcomputerSoftwareSelection (genetic algorithm)MathematicsSignal Processing: Image Communication
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A New Wavelet-Based Texture Descriptor for Image Retrieval

2007

This paper presents a novel texture descriptor based on the wavelet transform. First, we will consider vertical and horizontal coefficients at the same position as the components of a bivariate random vector. The magnitud and angle of these vectors are computed and its histograms are analyzed. This empirical magnitud histogram is modelled by using a gamma distribution (pdf). As a result, the feature extraction step consists of estimating the gamma parameters using the maxima likelihood estimator and computing the circular histograms of angles. The similarity measurement step is done by means of the well-known Kullback-Leibler divergence. Finally, retrieval experiments are done using the Bro…

Local binary patternsbusiness.industryTexture DescriptorFeature extractionComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONWavelet transformPattern recognitionComputingMethodologies_PATTERNRECOGNITIONWaveletImage textureComputer Science::Computer Vision and Pattern RecognitionHistogramArtificial intelligencebusinessImage retrievalMathematics
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IOWA Operators and Its Application to Image Retrieval

2014

This paper presents a relevance feedback procedure based on logistic regression analysis. Since, the dimension of the feature vector associated to each image is typically larger than the number of evaluated images by the user, different logistic regression models have to be fitted separately. Each fitted model provides us with a relevance probability and a confidence interval for that probability. In order to aggregate these set of probabilities and confidence intervals we use an IOWA operator. The results will show the success of our algorithm and that OWA operators are an efficient and natural way of dealing with this kind of fusion problems.

Operator (computer programming)Feature vectorRelevance feedbackRelevance (information retrieval)Data miningLogistic regressioncomputer.software_genreContent-based image retrievalcomputerImage retrievalConfidence intervalMathematics
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Shape Description for Content-Based Image Retrieval

2000

The present work is focused on a global image characterization based on a description of the 2D displacements of the different shapes present in the image, which can be employed for CBIR applications.To this aim, a recognition system has been developed, that detects automatically image ROIs containing single objects, and classifies them as belonging to a particular class of shapes.In our approach we make use of the eigenvalues of the covariance matrix computed from the pixel rows of a single ROI. These quantities are arranged in a vector form, and are classified using Support Vector Machines (SVMs). The selected feature allows us to recognize shapes in a robust fashion, despite rotations or…

PixelContextual image classificationbusiness.industryComputer scienceCovariance matrixComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingPattern recognitionContent-based image retrievalSupport vector machineComputingMethodologies_PATTERNRECOGNITIONFeature (computer vision)Computer Science::Computer Vision and Pattern RecognitionPattern recognition (psychology)Computer visionArtificial intelligencebusiness
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