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Video Retrieval and Sorting Algorithm based on Multiple Features in Sports Videos

Volume 14, Number 8, August 2018, pp. 1765-1773
DOI: 10.23940/ijpe.18.08.p13.17651773

Yanbo Su

Sports Department of Heilongjiang University, Harbin, 150080, China

(Submitted on April 25, 2018; Revised on June 13, 2018; Accepted on July 17, 2018)


Multimedia information, especially videos, is growing explosively with the rapid development of the Internet and multimedia technology. Due to its variety of image features, it is capable of reaching several hundred dimensions and even thousands of dimensions. Storing and indexing the high-dimensional feature vectors has become key technologies of content-based video retrieval. The residual quantization mechanism, which combines the asymmetric distance and set sorting algorithm based on multi-feature candidates, is improved after analyzing the characteristics of soccer videos. For soccer videos, SD-VLAD (Soft Distribution-Vectors of Locally Aggregated Descriptors), BOC (Bag of Color), and shot type are selected for describing the information of images. To address the problem that the original residual quantized inverted index can only retrieve single features, multiple feature retrieval and sorting are proposed. In the stage of candidate set sorting, a multi-feature based similarity calculation method is designed according to the shots type. The experimental results show that multi-feature hierarchical retrieval and sorting can be achieved at the cost of memory space. While ensuring query speed, the accuracy of the query is improved.


References: 10

              1. Y. G. Pu, “A Review of Research on the Key Technology of Content based Video Retrieval,” Information Science, Vol. 28, No. 3, pp. 464-469, 2010
              2. H. Jegou and D. Matthijs, “Aggregating Local Descriptors into a Compact Image Representation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3304-3311, San Francisco, USA, 2010
              3. O. Aude and T. Antonio, “Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope,” International Journal of Computer Vision, Vol. 42, No. 3, pp. 145-175, 2011
              4. O. Aude, “Building the Gist of a Scene: The Role of Global Image Features in Recognition,” in Proceedings of Progress in Brain Research, pp. 23-36, 2006
              5. M. Douze, “Evaluation of GIST Descriptors for Web-scale Image Search,” in Proceedings of the ACM International Conference on Image and Video Retrieval, pp. 1-8, 2009
              6. H. Jegou and C. Schmid, “Accurate Image Search using the Contextual Dissimilarity Measure,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 1, pp. 2-11, 2010
              7. P. James, C. Ondrej, and I. Michael, “Lost in Quantization: Improving Particular Object Retrieval in Large Scale Image Databases,” in Proceedings of the IEEE 9th Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008
              8. J. Michael, “Color Indexing,” International Journal of Computer Vision, Vol. 7, No. 1, pp. 11-32, 1991
              9. S. Josef and Z. Andrew, “Video Google: A Text Retrieval Approach to Object Matching in Videos,” in Proceedings of the IEEE 12th International Conference on Computer Vision, pp. 1470-1477, 2013
              10. P. Florent and M. Thomas, “Improving the Fisher Kernel for Large-scale Image Classification,” in Proceedings of the 11th European Conference on Computer Vision (ECCV), pp. 143-156, 2010


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