Data Dependent Adaptive Prediction and Classification of Video Sequences

  • Amrutha MachireddyEmail author
  • Shayan Srinivasa Garani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)


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.


Data-dependent pooling Adaptive network Prediction and classification network 



S. S. Garani acknowledges IISc-start up funds for this project.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronic Systems EngineeringIndian Institute of ScienceBengaluruIndia

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