Birinci-Şahıs Videolarda Aktivite Tanıma İçin Sıralamalı Takviyeli Çoklu Çekirdek Öğrenmesi

Date

2018-05-05

Author

Özkan, Fatih
Sürer, Elif
Temizel, Alptekin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this paper, we investigate fusion of different types of classifiers for activity recognition on first-person videos in a data-driven approach. The algorithm first uses the classifiers, which are composed of kernel and descriptor combinations, through well-known AdaBoost trials. After all trials, classifiers are ordered and assigned ranks with respect to their performances in each trial separately. These classifiers compose a candidate list according to their performance ranks. Classifiers in the candidate list are employed together on the training set again. Classifiers in most successful candidate lists are combined as final classifiers. Our experiments show improvements in recognition comparison to traditional methods.

Subject Keywords

Activity recognition, Multiple kernel learning, Boosting

URI

https://hdl.handle.net/11511/31248

DOI

https://doi.org/10.1109/siu.2018.8404221

Conference Name

26th IEEE Signal Processing and Communications Applications Conference (SIU)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

F. Özkan, E. Sürer, and A. Temizel, “Birinci-Şahıs Videolarda Aktivite Tanıma İçin Sıralamalı Takviyeli Çoklu Çekirdek Öğrenmesi,” presented at the 26th IEEE Signal Processing and Communications Applications Conference (SIU), Izmir, TURKEY, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31248.