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A novel shot boundary detection system using hybrid optimization technique

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Abstract

This paper proposes a novel shot boundary detection method which combines the Advantages of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) to optimize the weights of the Feed-Forward Neural Network (FNN). To increase the performance of the system, the output of the hybrid technique is again analyzed by forming a Continuity matrix (ϕ). Then an Outlier along with a Continuity matrix is used for extracting a possible set of transition frames. A set of thresholds δ1 and δ2 is selected for classifying abrupt and gradual transitions from the available set of possible transition frames. Experimental results using TRECVid 2001 depicts that PSOGSA outperforms GSA and PSO in-terms of training the feed forward neural network and generating a higher overall F1 score. The proposed system also gives better performance when compared with other latest techniques in-terms of F1 score.

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Authors and Affiliations

  1. Computer Vision Laboratory, Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, Assam, India

    Saptarshi Chakraborty & Dalton Meitei Thounaojam

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  1. Saptarshi Chakraborty
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  2. Dalton Meitei Thounaojam
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Correspondence to Saptarshi Chakraborty.

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Chakraborty, S., Thounaojam, D.M. A novel shot boundary detection system using hybrid optimization technique. Appl Intell 49, 3207–3220 (2019). https://doi.org/10.1007/s10489-019-01444-1

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