Abstract
The exponential increase in video-based information has made it challenging for users to search specific video from a huge database. In this paper, template-based video search engine is proposed to improve the retrieval efficiency and accuracy of search engines. To begin with, the system splits the video sequence into eight key frames and then the fused image is created. The visual features like color and texture are extracted from the fused image and stored as complete feature set in a database. Now, the query clip is selected from the query database and then the template image is selected from the fused query image. The template query image features are compared with stored feature database using various similarity measures. The relevant retrieval experiments show that template-based video search engine using wavelet-based feature extraction gives better result in terms of average precision and recall using Euclidean distance as a similarity measure.
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Gupta, S., Kulkarni, R.K. (2019). Template-Based Video Search Engine. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_27
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DOI: https://doi.org/10.1007/978-3-030-00665-5_27
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