Efficient Content Based Video Retrieval System by Applying AlexNet on Key Frames

• Altaf Hussain ^{[2] [1]} ; Mehtab Ahmad ^[2] ; Tariq Hussain ^{[2] [4]} ; Ijaz Ullah ^[3]
  1. [1] Qatar University

     Qatar University

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  2. [2] Institute of Computer Science & IT (ICS/IT), The University of Agriculture Peshawar, Pakistan
  3. [3] University of Rennes 1, France
  4. [4] School of Computer Science and Information Engineering, Zhejiang Gongshang University Hangzhou, China

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• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 11, Nº. 2, 2022, págs. 207-235
• Idioma: inglés
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  • The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.