Search results for "Automatic image annotation"

showing 8 items of 18 documents

Clustering techniques for personal photo album management

2009

In this work we propose a novel approach for the automatic representation of pictures achieving at more effective organization of personal photo albums. Images are analyzed and described in multiple representation spaces, namely, faces, background and time of capture. Faces are automatically detected, rectified and represented projecting the face itself in a common low-dimensional eigenspace. Backgrounds are represented with low-level visual features based on RGB histogram and Gabor filter bank. Faces, time and background information of each image in the collection is automatically organized using a mean-shift clustering technique. Given the particular domain of personal photo libraries, wh…

Gabor filterspattern clusteringComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONcontent-based retrievalFacial recognition systemimage retrievalimage colour analysisHistogramComputer visionimage representationElectrical and Electronic EngineeringCluster analysisImage retrievalMathematicsbusiness.industryCBIR - Content Based Image Retrieval automatic image annotation personal photo album managementPattern recognitionAtomic and Molecular Physics and OpticsComputer Science ApplicationsData setAutomatic image annotationFace (geometry)RGB color modelArtificial intelligenceeigenvalues and eigenfunctionsbusinessface recognitionJournal of Electronic Imaging
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<title>Combining multiple image descriptions for browsing and retrieval</title>

2000

Retrieving images form large collections using image content is an important problem, in this multimedia age. A quick content-based visual access to the stored image is capital for efficient navigation through image collections. In this paper we introduce several techniques which characterize color homogeneous object and their spatial relationships for efficient content-based image retrieval. We present a region growing technique for efficient color homogeneous objects segmentation and extend the 2D string to an accurate description of spatial information and relationships. In order to improve content-based image retrieval, our method emphasized several objectives, such as: automated extrac…

Information retrievalComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONContent-based image retrievalAutomatic image annotationImage textureRegion growingHuman–computer information retrievalComputer visionSegmentationVisual WordArtificial intelligencebusinessImage retrievalFeature detection (computer vision)SPIE Proceedings
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Automatic building of a visual interface for content-based multiresolution retrieval of paleontology images

2001

In this article we present research work in the field of content-based image retrieval in large databases applied to the paleontology image database of the Universite´ de Bourgogne, Dijon, France, called ‘‘TRANS’TYFIPAL.’’ Our indexing method is based on multiresolution decomposition of database images using wavelets. For each family of paleontology images we try to find a model image that represents it. The K-means automatic classification algorithm divides the space of parameters into several clusters. A model image for each cluster is computed from the wavelet transform of each image of the cluster. Then a search tree is built to offer users a graphic interface for retrieving images. So …

Information retrievalContextual image classificationComputer sciencebusiness.industrySearch engine indexingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION020206 networking & telecommunicationsImage processing02 engineering and technologyContent-based image retrievalAtomic and Molecular Physics and OpticsSearch treeComputer Science ApplicationsPaleontologyAutomatic image annotation[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionVisual WordArtificial intelligenceElectrical and Electronic EngineeringbusinessImage retrievalComputingMilieux_MISCELLANEOUS
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Content Based Indexing of Image and Video Databases by Global and Shape Features

1996

Indexing and retrieval methods based on the image content are required to effectively use information from the large repositories of digital images and videos currently available. Both global (colour, texture, motion, etc.) and local (object shape, etc.) features are needed to perform a reliable content based retrieval. We present a method for automatic extraction of global image features, like colour and motion parameters, and their use for data restriction in video database querying. Further retrieval is therefore accomplished, in a restricted set of images, by shape feature (skeleton, local symmetry moments, correlation, etc.) local search. The proposed indexing methodology has been deve…

Settore INF/01 - InformaticaComputer sciencebusiness.industrySearch engine indexingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCBIR video indexing image analysisDigital imageAutomatic image annotationImage textureFeature (computer vision)Computer visionLocal search (optimization)Visual WordArtificial intelligencebusinessImage retrieval
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Automatic image representation and clustering on mobile devices.

2010

In this paper a novel approach for the automatic representation of pictures on mobile devices is proposed. With the wide diffusion of mobile digital image acquisition devices, the need of managing a large number of digital images is quickly increasing. In fact the storage capacity of such devices allow users to store hundreds or even thousands, of pictures that, without a proper organization, become useless. Users may be interested in using (i.e., browsing, saving, printing and so on) a subset of stored data according to some particular picture properties. A content-based description of each picture is needed to perform on-board image indexing. In our work the images are analyzed and descri…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniCBIR automatic image annotation mobile devicesImage retrievalmean-shift clusteringpersonal photo albumPhoto collection
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A Conceptual Probabilistic Model for the Induction of Image Semantics

2010

In this paper we propose a model based on a conceptual space automatically induced from data. The model is inspired to a well-founded robotics cognitive architecture which is organized in three computational areas: sub-conceptual, linguistic and conceptual. Images are objects in the sub-conceptual area, that become "knoxels" into the conceptual area. The application of the framework grants the automatic emerging of image semantics into the linguistic area. The core of the model is a conceptual space induced automatically from a set of annotated images that exploits and mixes different information concerning the set of images. Multiple low level features are extracted to represent images and…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniImage ClassificationComputer sciencebusiness.industryFeature extractionimage semantics conceptual spaceConceptual model (computer science)Statistical modelcomputer.software_genreConceptual schemaVisualizationSet (abstract data type)Data setAutomatic image annotationLatent Semantic AnalysisArtificial intelligencebusinesscomputerNatural language processing
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Distance-based functions for image comparison

1999

The interest in digital image comparison is steadily growing in the computer vision community. The definition of a suitable comparison measure for non-binary images is relevant in many image processing applications. Visual tasks like segmentation and classification require the evaluation of equivalence classes. Measures of similarity are also used to evaluate lossy compression algorithms and to define pictorial indices in image content based retrieval methods. In this paper we develop a distance-based approach to image similarity evaluation and we present several image distances which are based on low level features. The sensitivity and eAectiveness are tested on real data. ” 1999 Published…

Standard test imagebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingPattern recognitionImage segmentationAutomatic image annotationImage textureArtificial IntelligenceSignal ProcessingDigital image processingComputer visionComputer Vision and Pattern RecognitionArtificial intelligencebusinessImage retrievalSoftwareMathematicsFeature detection (computer vision)Pattern Recognition Letters
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Ontology-driven Image Analysis for Histopathological Images

2010

International audience; Ontology-based software and image processing engine must cooperate in new fields of computer vision like microscopy acquisition wherein the amount of data, concepts and processing to be handled must be properly controlled. Within our own platform, we need to extract biological objects of interest in huge size and high-content microscopy images. In addition to specific low-level image analysis procedures, we used knowledge formalization tools and high-level reasoning ability of ontology-based software. This methodology made it possible to improve the expressiveness of the clinical models, the usability of the platform for the pathologist and the sensitivity or sensibi…

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer science[INFO.INFO-IM] Computer Science [cs]/Medical ImagingComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONImage processingOntology (information science)[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]030218 nuclear medicine & medical imaging03 medical and health sciences[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]0302 clinical medicineSoftware[STAT.AP] Statistics [stat]/Applications [stat.AP][INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingDigital image processing[ INFO.INFO-TI ] Computer Science [cs]/Image Processing[INFO.INFO-IM]Computer Science [cs]/Medical ImagingComputer visionRDFImage analysis[STAT.AP]Statistics [stat]/Applications [stat.AP]Information retrieval[ INFO.INFO-IM ] Computer Science [cs]/Medical Imagingbusiness.industry[ STAT.AP ] Statistics [stat]/Applications [stat.AP][INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Usabilitycomputer.file_formatAutomatic image annotation[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]030220 oncology & carcinogenesis[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]Artificial intelligencebusinesscomputer
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