Implementing Content Based Video Retrieval Using Speeded-Up Robust Features
Jos Timanta Tarigan, Poltak Sihombing and Evi Marpaung (Universitas Sumatera Utara, Indonesia)
In this paper, we propose an implementation of Content-Based Video Retrieval (CBVR) using Speeded-Up Robust Features (SURF). Given an image as a query, the application looks through a set of videos and pick the ones contain frame similar to the image query. Our objective is to measure the performance of the algorithm. The performance is measured using three variables: recall, precision, and running time. We used two sets of samples to perform the test: in-frame and not-in-frame. Furthermore, we limit the samples only to contain these 5 categories: body parts, kitchen and eating utensils, fruits, and pets. The test shows the program gives a 57.75% average recall value and 37.5% precision value for not-in-frame test, while the in-frame-test gives 51% and 59% for recall and precision value respectively. Moreover, the running time data shows there is no relation between in-frame/not-in-frame and speed. Running time performance highly depends on the image query and the length of the video.
Journal: International Journal of Simulation: Systems, Science and Technology IJSSST V19
Published: Jun 30, 2018