Data dependent adaptive prediction and classification of video sequences

Machireddy, A and Garani, SS (2018) Data dependent adaptive prediction and classification of video sequences. In: 17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018, 3 June 2018 through 7 June 2018, Zakopane, pp. 136-147.

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Abstract

Convolutional neural networks (CNN) are popularly used for applications in natural language processing, video analysis and image recognition. However, the max-pooling layer used in CNNs discards most of the data, which is a drawback in applications, such as, prediction of video frames. With this in mind, we propose an adaptive prediction and classification network (APCN) based on a data-dependent pooling architecture. We formulate a combined cost function for minimizing prediction and classification errors. During testing, we identify a new class in an unsupervised fashion. Simulation results over a synthetic data set show that the APCN algorithm is able to learn the spatio-temporal information to predict and classify the video frames, as well as, identify a new class during testing. © Springer International Publishing AG, part of Springer Nature 2018.

Item Type:	Conference Paper
Publication:	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher:	Springer Verlag
Additional Information:	The copyright for this article belongs to the Springer International Publishing AG, part of Springer Nature.
Keywords:	Cost functions; Forecasting; Image recognition; Natural language processing systems; Neural networks; Soft computing; Statistical tests, Adaptive networks; Adaptive predictions; Classification errors; Classification networks; Convolutional Neural Networks (CNN); Data dependent; Spatiotemporal information; Synthetic datasets, Classification (of information)
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	26 Aug 2022 06:12
Last Modified:	26 Aug 2022 06:12
URI:	https://eprints.iisc.ac.in/id/eprint/76060

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