16,145 research outputs found

    Content Based Image Retrieval System Using NOHIS-tree

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    Content-based image retrieval (CBIR) has been one of the most important research areas in computer vision. It is a widely used method for searching images in huge databases. In this paper we present a CBIR system called NOHIS-Search. The system is based on the indexing technique NOHIS-tree. The two phases of the system are described and the performance of the system is illustrated with the image database ImagEval. NOHIS-Search system was compared to other two CBIR systems; the first that using PDDP indexing algorithm and the second system is that using the sequential search. Results show that NOHIS-Search system outperforms the two other systems.Comment: 6 pages, 10th International Conference on Advances in Mobile Computing & Multimedia (MoMM2012

    Low Level Processing of Audio and Video Information for Extracting the Semantics of Content

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    The problem of semantic indexing of multimedia documents is actually of great interest due to the wide diffusion of large audio-video databases. We first briefly describe some techniques used to extract low-level features (e.g., shot change detection, dominant color extraction, audio classification etc.). Then the ToCAI (table of contents and analytical index) framework for content description of multimedia material is presented, together with an application which implements it. Finally we propose two algorithms suitable for extracting the high level semantics of a multimedia document. The first is based on finite-state machines and low-level motion indices, whereas the second uses hidden Markov models

    Multimodal Databases

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    Práce je věnována tématu multimodálních databází se zaměřením na multimediální data. První část práce obsahuje výklad dnes nejpoužívanějších modelů pro ukládání dat. Další část pak shrnuje poznatky z oblasti podobnostního vyhledávání v multimediálním obsahu a indexace tohoto typu dat. Závěr je věnovaný implementaci systému pro správu a uchovávání multimediálního obsahu založeném na systémech Helix a PostgreSQL.This masters thesis is dedicated to theme of multimodal databases, especially multimedia databases. The first part contains overview of today's most used data models. Next part summarizes information about content-based search in multimedia content and indexing this type of data. Final part is dedicated to implementation system for storing and managing of multimedia content based on Helix streaming system a PostgreSQL database system.

    Hierarchical linear subspace indexing method

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    Traditional multimedia indexing methods are based on the principle of hierarchical clustering of the data space where metric properties are used to build a tree that can then be used to prune branches while processing the queries. However, the performance of these methods will deteriorate rapidly when the dimensionality of the data space is increased. We describe a new hierarchical linear subspace indexing method will based on the generic multimedia indexing (GEMINI) approach, which does not suffer from the dimensionality problem. The hierarchical subspace approach offers a fast searching method for large content-based multimedia databases. The approach will be demonstrated on image indexing, in which the subspaces correspond to different resolutions of the images. During content-based image retrieval the search starts in the subspace with the lowest resolution of the images. In this subspace the set off all possible similar images is determined. In the next subspace additional metric information corresponding to a higher resolution is used to reduce this set. This procedure is repeated until the similar images can be determined eliminating the false candidates

    Automatic detection and extraction of artificial text in video

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    A significant challenge in large multimedia databases is the provision of efficient means for semantic indexing and retrieval of visual information. Artificial text in video is normally generated in order to supplement or summarise the visual content and thus is an important carrier of information that is highly relevant to the content of the video. As such, it is a potential ready-to-use source of semantic information. In this paper we present an algorithm for detection and localisation of artificial text in video using a horizontal difference magnitude measure and morphological processing. The result of character segmentation, based on a modified version of the Wolf-Jolion algorithm [1][2] is enhanced using smoothing and multiple binarisation. The output text is input to an “off-the-shelf” noncommercial OCR. Detection, localisation and recognition results for a 20min long MPEG-1 encoded television programme are presented

    Post processing of multimedia information - concepts, problems, and techniques

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    Currently, most research work on multimedia information processing is focused on multimedia information storage and retrieval, especially indexing and content-based access of multimedia information. We consider multimedia information processing should include one more level-post-processing. Here &quot;post-processing&quot; means further processing of retrieved multimedia information, which includes fusion of multimedia information and reasoning with multimedia information to reach new conclusions. In this paper, the three levels of multimedia information processing storage, retrieval, and post-processing- are discussed. The concepts and problems of multimedia information post-processing are identified. Potential techniques that can be used in post-processing are suggested, By highlighting the problems in multimedia information post-processing, hopefully this paper will stimulate further research on this important but ignored topic.<br /

    Image mining: trends and developments

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    [Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

    Advanced content-based semantic scene analysis and information retrieval: the SCHEMA project

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    The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: content-based multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic -web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will be integrated in the SCHEMA module-based, expandable reference system

    Image mining: issues, frameworks and techniques

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    [Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper

    The relationship between IR and multimedia databases

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    Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud \ud Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud \ud Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud \ud First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud \ud Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud \ud Third, we add the functionality to process the users' relevance feedback.\ud \ud We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud \ud We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
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