Stochastic recursive gradient descent optimization-based on foreground features of Fisher vector

Mohamed Gamal M. Kamaleldin; Syed A. R. Abu-Bakar; Usman Ullah Sheikh

doi:10.1117/12.2644640

12 October 2022 Stochastic recursive gradient descent optimization-based on foreground features of Fisher vector

Mohamed Gamal M. Kamaleldin, Syed A. R. Abu-Bakar, Usman Ullah Sheikh

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 1234204 (2022) https://doi.org/10.1117/12.2644640
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance , first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51.

Citation Download Citation

Mohamed Gamal M. Kamaleldin, Syed A. R. Abu-Bakar, and Usman Ullah Sheikh "Stochastic recursive gradient descent optimization-based on foreground features of Fisher vector", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 1234204 (12 October 2022); https://doi.org/10.1117/12.2644640

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KEYWORDS

Stochastic processes

Data modeling

Optimization (mathematics)

Feature extraction

Mathematical modeling

Performance modeling

Machine vision

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