The Experts below are selected from a list of 102837 Experts worldwide ranked by ideXlab platform
Ahmed K. Elmagarmid - One of the best experts on this subject based on the ideXlab platform.
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concept oriented indexing of Video Databases toward semantic sensitive retrieval and browsing
IEEE Transactions on Image Processing, 2004Co-Authors: Ahmed K. ElmagarmidAbstract:Digital Video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based Video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic Video concept modeling, semantic Video classification, and concept-oriented Video Database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive Video content representation framework by using principal Video shots to enhance the quality of features; 2) semantic Video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic Video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented Video Database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive Video retrieval and browsing.
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a hierarchical access control model for Video Database systems
ACM Transactions on Information Systems, 2003Co-Authors: Elisa Bertino, Ahmed K. Elmagarmid, Elena Ferrari, Mohand-said Hacid, Jianping Fan, Xingquan ZhuAbstract:Content-based Video Database access control is becoming very important, but it depends on the progresses of the following related research issues: (a) efficient Video analysis for supporting semantic visual concept representation; (b) effective Video Database indexing structure; (c) the development of suitable Video Database models; and (d) the development of access control models tailored to the characteristics of Video data. In this paper, we propose a novel approach to support multilevel access control in Video Databases. Our access control technique combines a Video Database indexing mechanism with a hierarchical organization of visual concepts (i.e., Video Database indexing units), so that different classes of users can access different Video elements or even the same Video element with different quality levels according to their permissions. These Video elements, which, in our access control mechanism, are used for specifying the authorization objects, can be a semantic cluster, a subcluster, a Video scene, a Video shot, a Video frame, or even a salient object (i.e., region of interest). In the paper, we first introduce our techniques for obtaining these multilevel Video access units. We also propose a hierarchical Video Database indexing technique to support our multilevel Video access control mechanism. Then, we present an innovative access control model which is able to support flexible multilevel access control to Video elements. Moreover, the application of our multilevel Video Database modeling, representation, and indexing for MPEG-7 is discussed.
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medical Video mining for efficient Database indexing management and access
International Conference on Data Engineering, 2003Co-Authors: Xingquan Zhu, Jianping Fan, Walid G. Aref, Ann Christine Catlin, Ahmed K. ElmagarmidAbstract:To achieve more efficient Video indexing and access, we introduce a Video Database management framework and strategies for Video content structure and events mining. The Video shot segmentation and representative frame selection strategy are first utilized to parse the continuous Video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the Video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and Video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired Video content structure and events are integrated to construct a scalable Video skimming tool which can be used to visualize the Video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.
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Video Query Processing in the VDBMS Testbed For Video Database Research
2003Co-Authors: Walid G. Aref, Ahmed K. Elmagarmid, Ann C. Catlin, Moustafa Hammad, Ihab F. Ilyas, Mirette Marzouk, Thanaa GhanemAbstract:The increased use of Video data sets for multimedia-based applications has created a demand for strong Video Database support, including efficient methods for handling the contentbased query and retrieval of Video data. Video query processing presents significant research challenges, mainly associated with the size, complexity and unstructured nature of Video data. A Video query processor must support Video operations for search by content and streaming, new query types, and the incorporation of Video methods and operators in generating, optimizing and executing query plans. In this paper, we address these query processing issues in two contexts, first as applied to the Video data type and then as applied to the stream data type. We present the query processing functionality of the VDBMS Video Database management system, which was designed to support a full range of functionality for Video as an abstract data type. We describe two query operators for the Video data type which implement the rank-join and stop-after algorithms. As Videos may be considered streams of consecutive image frames, Video query processing can be expressed as continuous queries over Video data streams. The stream data type was therefore introduced into the VDBMS system, and system functionality was extended to support general data streams. From this viewpoint, we present an approach for defining and processing streams, including Video, through the query execution engine. We describe the implementation of several algorithms for Video query processing expressed as continuous queries over Video streams, such as fast forward, region-based blurring and left outer join. We include a description of the window-join algorithm as a core operator for continuous query systems, and discuss shared execution as ..
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smart Videotext a Video data model based on conceptual graphs
Multimedia Systems, 2002Co-Authors: Fotios Kokkoras, Haitao Jiang, Ahmed K. Elmagarmid, Ioannis Vlahavas, Elias N Houstis, Walid G. ArefAbstract:An intelligent annotation-based Video data model called Smart VideoText is introduced. It utilizes the conceptual graph knowledge representation formalism to capture the semantic associations among the concepts described in text annotations of Video data. The aim is to achieve more effective query, retrieval, and browsing capabilities based on the semantic content of Video data. Finally, a generic and modular Video Database architecture based on the Smart VideoText data model is described.
Yasutaka Furukawa - One of the best experts on this subject based on the ideXlab platform.
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planenet piece wise planar reconstruction from a single rgb image
Computer Vision and Pattern Recognition, 2018Co-Authors: Chen Liu, Jimei Yang, Duygu Ceylan, Ersin Yumer, Yasutaka FurukawaAbstract:This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image depth prediction, piece-wise planar depthmap reconstruction requires a structured geometry representation, and has been a difficult task to master even for DNNs. The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image. We have generated more than 50,000 piece-wise planar depthmaps for training and testing from ScanNet, a large-scale RGBD Video Database. Our qualitative and quantitative evaluations demonstrate that the proposed approach outperforms baseline methods in terms of both plane segmentation and depth estimation accuracy. To the best of our knowledge, this paper presents the first end-to-end neural architecture for piece-wise planar reconstruction from a single RGB image. Code and data are available at https://github.com/art-programmer/PlaneNet.
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PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Chen Liu, Jimei Yang, Duygu Ceylan, Ersin Yumer, Yasutaka FurukawaAbstract:This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image depth prediction, piece-wise planar depthmap reconstruction requires a structured geometry representation, and has been a difficult task to master even for DNNs. The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image. We have generated more than 50,000 piece-wise planar depthmaps for training and testing from ScanNet, a large-scale RGBD Video Database. Our qualitative and quantitative evaluations demonstrate that the proposed approach outperforms baseline methods in terms of both plane segmentation and depth estimation accuracy. To the best of our knowledge, this paper presents the first end-to-end neural architecture for piece-wise planar reconstruction from a single RGB image. Code and data are available at this https URL.
Ioannis Pitas - One of the best experts on this subject based on the ideXlab platform.
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view indepedent human movement recognition from multi view Video exploiting a circular invariant posture representation
International Conference on Multimedia and Expo, 2009Co-Authors: Nikolaos Gkalelis, Nikos Nikolaidis, Ioannis PitasAbstract:In this paper a novel method for view independent human movement representation and recognition, exploiting the rich information contained in multi-view Videos, is proposed. The binary masks of a multi-view posture image are first vectorized, concatenated and the view correspondence problem between train and test samples is solved using the circular shift invariance property of the discrete Fourier transform (DFT) magnitudes. Then, using fuzzy vector quantization (FVQ) and linear discriminant analysis (LDA), different movements are represented and classified. This method allows view independent movement recognition, without the use of calibrated cameras, a-priori view correspondence information or 3D model reconstruction. A multiview Video Database has been constructed for the assessment of the proposed algorithm. Evaluation of this algorithm on the new Database, shows that it is particularly efficient and robust, and can achieve good recognition performance.
Cheng Zheng - One of the best experts on this subject based on the ideXlab platform.
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a novel framework for concept detection on large scale Video Database and feature pool
Artificial Intelligence Review, 2013Co-Authors: Cheng ZhengAbstract:Large-scale semantic concept detection from large Video Database suffers from large variations among different semantic concepts as well as their corresponding effective low-level features. In this paper, we propose a novel framework to deal with this obstacle. The proposed framework consists of four major components: feature pool construction, pre-filtering, modeling, and classification. First, a large low-level feature pool is constructed, from which a specific set of features are selected for the latter steps automatically or semi-automatically. Then, to deal with the unbalance problem in training set, a pre-filtering classifier is generated, which the aim of achieving a high recall rate and a certain precision rate nearly 50% for a certain concept. Thereafter, from the pre-filtered training samples, a SVM classifier is built based on the selected features in the feature pool. After that, the SVM classifier is applied to classification of semantic concept. This framework is flexible and extensible in terms of adding new features into the feature pool, introducing human interactions in selecting features, building models for new concepts and adopting active learning.
F Golshani - One of the best experts on this subject based on the ideXlab platform.
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rx for semantic Video Database retrieval
ACM Multimedia, 1994Co-Authors: Nevenka Dimitrova, F GolshaniAbstract:The most prominent difference between still images and moving pictures stems from movements and variations. Thus to go from the realm of still image repositories to Video Databases, we must be able to deal with motion. Particularly, we need the ability to classify objects appearing in a Video sequence based on the movements of each object, as well as their other characteristics and features such as shape or color.By describing motion derived from motion analysis, we introduce a dual hierarchy consisting of spatial and temporal parts for Video sequence representation. This gives us the flexibility to examine arbitrary frames at various levels of abstraction, and to retrieve the associated temporal information (say, object trajectories) in addition to the spatial representation. Our algorithm for motion detection uses the motion compensation component of the MPEG Video encoding scheme. The algorithm then computes trajectories for objects of interest. The specification of a language for retrieval of Video based on the spatial as well as motion characteristics is presented.
