Video Summarization Using R-Sequences |
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| Affiliation: | 1. Centro Universitario de la Defensa, Zaragoza, Spain;2. Instituto de Ciencia de Materiales de Aragón (ICMA), CSIC – Universidad de Zaragoza, Zaragoza, Spain;3. Dpto. de Física de la Materia Condensada, Universidad de Zaragoza, Zaragoza, Spain;4. Departamento de Física, Universidad de Oviedo, 33007 Oviedo, Spain;5. Centro de Investigación en Nanomateriales y Nanotecnología, CINN (CSIC – Universidad de Oviedo), 33940 El Entrego, Spain;6. Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, 12489 Berlin, Germany;7. IN-IFIMUP, Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, 4169-007, Porto, Portugal;8. Instituto de Nanociencia de Aragón (INA) and Laboratorio de Microscopías Avanzadas (LMA), Universidad de Zaragoza, Zaragoza, Spain;1. Materials Science and Technology Division, National Institute for Interdisciplinary Science and Technology (NIIST-CSIR), Thiruvananthapuram 695019, India;2. Academy of Science and Innovative Research (AcSIR), India;3. Department of Electronics, Cochin University of Science and Technology, Cochin 682022, Kerala, India;1. Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, Australia;2. College of Engineering and Science, Victoria University, Melbourne, Australia;1. Department of Computer Science & Engineering, Kyung Hee University (Global Campus), 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do, 446–701, Korea;2. Department of Multimedia Science, Sookmyung Women’s University, Cheongpa-ro 47-gil 100 (Cheongpa-dong 2ga), Yongsan-gu, Seoul, 04310, Korea |
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| Abstract: | In this paper, we propose a new method of temporal summarization of digital video. First, we address the problem of extracting a fixed number of representative frames to summarize a given digital video. To solve it, we have devised an algorithm called content-based adaptive clustering (CBAC). In our algorithm, shot boundary detection is not needed. Video frames are treated as points in the multi-dimensional feature space corresponding to a low-level feature such as color, motion, shape and texture. The changes of their distances are compared globally for extraction of representative frames. Second, we address how to use the representative frames to comprise representative sequences (R - Sequence) which can be used for temporal summarization of video. A video player based on our devised algorithm is developed which has functions of content-based browsing and content-based video summary. Experiments are also shown in the paper. |
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