A Fast Feature Extraction Algorithm for Image and Video Processing

Sadiq H. Abdulhussain, Abd Rahman Ramli, Basheera M. Mahmmod, M. Iqbal Saripan, S. A.R. Al-Haddad, Thar Baker, Wameedh N. Flayyih, Wissam A. Jassim

Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


Medical images and videos are utilized to discover, diagnose and treat diseases. Managing, storing, and retrieving stored images effectively are considered important topics. The rapid growth of multimedia data, including medical images and videos, has caused a swift rise in data transmission volume and repository size. Multimedia data contains useful information; however, it consumes an enormous storage space. Therefore, high processing time for that sheer volume of data will be required. Image and video applications demand for reduction in computational cost (processing time) when extracting features. This paper introduces a novel method to compute transform coefficients (features) from images or video frames. These features are used to represent the local visual content of images and video frames. We compared the proposed method with the traditional approach of feature extraction using a standard image technique. Furthermore, the proposed method is employed for shot boundary detection (SBD) applications to detect transitions in video frames. The standard TRECVID 2005, 2006, and 2007 video datasets are used to evaluate the performance of the SBD applications. The achieved results show that the proposed algorithm significantly reduces the computational cost in comparison to the traditional method.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
Publication statusPublished - Jul 2019
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 14 Jul 201919 Jul 2019

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2019 International Joint Conference on Neural Networks, IJCNN 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


  • Abrupt Transition Detection
  • Block processing
  • Feature Extraction
  • Orthogonal Polynomials
  • Shot Boundary Detection
  • Temporal Video Segmentation


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