Indexing Method

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Murat A Tekalp - One of the best experts on this subject based on the ideXlab platform.

  • motion and shape signatures for object based Indexing of mpeg 4 compressed video
    International Conference on Acoustics Speech and Signal Processing, 1997
    Co-Authors: Mufit A Ferman, Bilge Gunsel, Murat A Tekalp
    Abstract:

    The emerging MPEG-4 standard enables direct access to individual objects in the video stream, along with boundary/shape, texture, and motion information about each object. This paper proposes an object-based video Indexing Method that is directly applicable to the MPEG-4 compressed video bitstreams. The Method aims to provide object-based content-interactivity; thus, defines the audio-visual object as the Indexing unit. The scheme involves object-based temporal segmentation of the video bit-stream, selection of key-frames and key-video-object-planes, and characterization of the motion and/or shape of each video object including the background object. We also propose syntax and semantics for an Indexing field to meet the content-based access requirement of MPEG-4. Experimental results are shown on two MPEG-4 test sequences.

D.g. Lowe - One of the best experts on this subject based on the ideXlab platform.

  • Object recognition from local scale-invariant features
    Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999
    Co-Authors: D.g. Lowe
    Abstract:

    An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor Indexing Method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds

Kwang Woo Nam - One of the best experts on this subject based on the ideXlab platform.

  • Indexing for efficient managing current and past trajectory of moving object
    Lecture Notes in Computer Science, 2004
    Co-Authors: Lee Eung Jae, Keun Ho Ryu, Kwang Woo Nam
    Abstract:

    For efficiently managing of moving object trajectories, several Indexing Methods have been proposed such as 3DR-tree, STR-tree, TB-tree. These Indexing Methods have been focused on only improving query performance. However, due to property of continuously changing its position, moving objects generate massive data and have to update its information frequently. Therefore, in this paper, we proposed an Indexing Method for managing current and past trajectories of moving objects efficiently. The proposed Method employed auxiliary cache for efficient updating performance. We also proposed the Method which can be reduced the index size by removing redundant information for expression of moving object trajectory. We showed that the proposed Method has not only the better performance for update and much smaller size of index size, but also almost same performance for the query compared to previous Indexing Method.

Donghong Han - One of the best experts on this subject based on the ideXlab platform.

  • a hyperplane based Indexing technique for high dimensional data
    Information Sciences, 2007
    Co-Authors: Guoren Wang, Xiangmin Zhou, Bin Wang, Baiyou Qiao, Donghong Han
    Abstract:

    In this paper, we propose a novel hyperplane based Indexing Method to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the key dimension based index. Compared with the key dimension concept, the hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Extensive experiments based on two types of real data sets are conducted and the results illustrate a significantly improved filtering efficiency. Because of the feature of hyperplane, the proposed Indexing Method is only suitable to Euclidean spaces.

C.w. Chung - One of the best experts on this subject based on the ideXlab platform.

  • An efficient Indexing Method for nearest neighbor searches in high-dirnensional image databases
    IEEE Transactions on Multimedia, 2002
    Co-Authors: Guang-ho Cha, Xiaoming Zhu, P. Petkovic, C.w. Chung
    Abstract:

    Nearest neighbor (NN) search is emerging as an important search paradigm in a variety of applications in which objects are represented as vectors of d numeric features. However, despite decades of efforts, except for the filtering approach such as the VA-file, the current solutions to find exact kNNs are far from satisfactory for large d. The filtering approach represents vectors as compact approximations and by first scanning these smaller approximations, only a small fraction of the real vectors are visited. In this paper, we introduce the local polar coordinate file (LPC-file) using the filtering approach for nearest-neighbor searches in high-dimensional image databases. The basic idea is to partition the vector space into rectangular cells and then to approximate vectors by polar coordinates on the partitioned local cells. The LPC information significantly enhances the discriminatory power of the approximation. To demonstrate the effectiveness of the LPC-file, we conducted extensive experiments and compared the performance with the VA-file and the sequential scan by using synthetic and real data sets. The experimental results demonstrate that the LPC-file outperforms both of the VA-file and the sequential scan in total elapsed time and in the number of disk accesses and that the LPC-file is robust in both "good" distributions (such as random) and "bad" distributions (such as skewed and clustered).