Murtaza, FizaYousaf, Muhammad HaroonVelastin Carroza, Sergio Alejandro2019-10-012019-10-012018-09-06Murtaza, F., Yousaf, M.H. y Velastin, S.A. (2018). DA-VLAD: Discriminative Action Vector Of Locally Aggregated Descriptors for Action Recognition. In 2018 25th IEEE International Conference on Image Processing (ICIP).978-1-4799-7061-2https://hdl.handle.net/10016/28937This paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP 2018)In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights. We use these discriminative action weights with standard VLAD encoding as a contribution of each codeword. DA-VLAD reduces the inter-class similarity efficiently by diminishing the effect of common codewords among multiple action classes during the encoding process. We present the effectiveness of DA-VLAD on two challenging action recognition datasets: UCF101 and HMDB51, improving the state-of-the-art with accuracies of 95.1% and 80.1% respectively.5eng© 2018 IEEE.Human action recognitionVLADFeature encodingCodewordsImproved Dense Trajectories (Idt)conference paperInformáticahttps://doi.org/10.1109/ICIP.2018.8451255open access2018 25th IEEE International Conference on Image Processing (ICIP)CC/0000029127