Classifying web videos using a global video descriptor

    Authors

    B. Solmaz; S. M. Assari;M. Shah

    Comments

    Authors: contact us about adding a copy of your work at [email protected]

    Abbreviated Journal Title

    Mach. Vis. Appl.

    Keywords

    Video descriptors; Action recognition; Frequency spectrum; Spatio-temporal analysis; RECOGNITION; Computer Science, Artificial Intelligence; Computer Science, ; Cybernetics; Engineering, Electrical & Electronic

    Abstract

    Computing descriptors for videos is a crucial task in computer vision. In this paper, we propose a global video descriptor for classification of videos. Our method, bypasses the detection of interest points, the extraction of local video descriptors and the quantization of descriptors into a code book; it represents each video sequence as a single feature vector. Our global descriptor is computed by applying a bank of 3-D spatio-temporal filters on the frequency spectrum of a video sequence; hence, it integrates the information about the motion and scene structure. We tested our approach on three datasets, KTH (Schuldt et al., Proceedings of the 17th international conference on, pattern recognition (ICPR'04), vol. 3, pp. 32-36, 2004), UCF50 (http://vision.eecs.ucf.edu/datasetsActions.html) and HMDB51 (Kuehne et al., HMDB: a large video database for human motion recognition, 2011), and obtained promising results which demonstrate the robustness and the discriminative power of our global video descriptor for classifying videos of various actions. In addition, the combination of our global descriptor and a local descriptor resulted in the highest classification accuracies on UCF50 and HMDB51 datasets.

    Journal Title

    Machine Vision and Applications

    Volume

    24

    Issue/Number

    7

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    1473

    Last Page

    1485

    WOS Identifier

    WOS:000324499000013

    ISSN

    0932-8092

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