CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Hadamard based Video Key Frame Extraction using Thepade's Transform Error Vector Rotation with Assorted Similarity Measures

by Pritam H. Patil, Sudeep D. Thepade, Babita Sonare
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 5
Year of Publication: 2015
Authors: Pritam H. Patil, Sudeep D. Thepade, Babita Sonare
10.5120/21699-4810

Pritam H. Patil, Sudeep D. Thepade, Babita Sonare . Hadamard based Video Key Frame Extraction using Thepade's Transform Error Vector Rotation with Assorted Similarity Measures. International Journal of Computer Applications. 122, 5 ( July 2015), 36-40. DOI=10.5120/21699-4810

@article{ 10.5120/21699-4810,
author = { Pritam H. Patil, Sudeep D. Thepade, Babita Sonare },
title = { Hadamard based Video Key Frame Extraction using Thepade's Transform Error Vector Rotation with Assorted Similarity Measures },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 5 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number5/21699-4810/ },
doi = { 10.5120/21699-4810 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:09:48.550140+05:30
%A Pritam H. Patil
%A Sudeep D. Thepade
%A Babita Sonare
%T Hadamard based Video Key Frame Extraction using Thepade's Transform Error Vector Rotation with Assorted Similarity Measures
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 5
%P 36-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In Video summarization is a method to reduce redundancy and generate succinct representation of the video data. In video summarization process, several frames containing similar information need to get processed, this leads to slower processing speed and higher complexity, consuming. More time Video summarization using key frames can ease the speed up of video processing. One of the mechanisms to generate video summaries is to extract key frames which represent the most important content of the video by identifying neard duplicate frames in video. In this paper, novel key frames extraction method is proposed with Thepade's Walsh Hademard Error Vector Rotation (THdEVR) with ten different codebook sizes and and assorted similarity measures. Experimentation done with help of the test bed of videos has shown that higher codebook sizes give better completeness in key frame extraction for video summarization. Experimental results are discussed for video content summarization with five assorted similarity measures like Euclidean Distance, Canberra Distance, Square-Chord Distance, Mean Square Error, Sorensen Distance with proposed THadVR.

References
  1. Z. Xiong, X. S. Zhou, Q. Tian, Y. Rui, and H. TS, "Semantic retrieval of video - review of research on video retrieval in meetings, movies and broadcast news, and sports," IEEE Signal Processing Magazine, vol. 23, no. 2, pp. 18–27, march 2006.
  2. V. Valdes and J. Martinez, "Efficient video summarization and retrieval tools," in 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI), june 2011, pp. 43–48.
  3. M. Furini, F. Geraci, M. Montangero, M. Pellegrini, a1, a2, and a3, "Stimo: Still and moving video storyboard for the web scenario," Multimedia Tools Appl. , vol. 46, no. 1, pp. 47–69, Jan. 2010.
  4. Edward J. Y. Cayllahua Cahuina, Guillermo Camara Chavez," A New Method for Static Video Summarization Using Local Descriptors and Video Temporal Segmentation", IEEE Conference on Graphics, Patterns and Images (SIBGRAPI)XXVI. 2013, pages 226 - 233.
  5. B. T. Truong and S. Venkatesh, "Video abstraction: A systematic review and classification," ACM Transactions on Multimedia Computing, Communications and Applications, 2007.
  6. A. G. Money and H. Agius, "Video summarisation: A conceptual framework and survey of the state of the art," Journal of Visual Communication and Image Representation, pp. 121–143, 2008.
  7. Y. Gao, W. -B. Wang, J. -H. Yong, and H. -J. Gu, "Dynamic video summarization using two-level redundancy detection," Multimedia Tools and Applications, pp. 233–250, 2009.
  8. Y. Linde, A. Buzo, R. M. Gray. "An algorithm for vector quantizer design". IEEE Trans. Commun. , COM-28(1):84-95, 1980.
  9. Sudeep D. Thepade, Pritam H. Patil, "Novel Keyframe Extraction for Video Content Summarization using LBG Codebook Generation Technique of Vector Quantization", International Journal of Computer Applications, Volume 111, Number 9, pp. -49-53, ISBN: 973-93-80885-22-9, Feb-2015
  10. Dr. Sudeep Thepade, Vandana Mhaske," New Clustering Algorithm for Vector Quantization using Haar Sequence", International and Communication Technologies (ICT), pp-1144-1149, April 2013.
  11. Sudeep D. Thepade, Pritam H. Patil, ''Novel Visual Content summarization in Videos using Keyframe Extraction with Thepade's Sorted Ternary Block Truncation Coding and Assorted Similarity Measures'', International Conference on Communication, Information & Computing Technology (ICCICT), Jan. 16-17, Mumbai, India pp. 1-5, 2015.
  12. Sung-Hyuk Cha, "Comprehensive Survey on Distance/Similarity Measures betwwen Probability Density Functions", International Journal of Mathematical Models and methods in Applied Sciences, Issue 4, Volume 1, 2007(300-307).
  13. Dr. H. B. Kekre , Dr. Sudeep , D. Thepade , Saurabh Gupta, "Content Based Video Retrieval in Transformed Domain using Fractional Coefficients", International Journal of Image Processing (IJIP), Volume (7) : Issue (3) : 2013
  14. Dr. H. B. Kekre, Sudeep D. Thepade,Varun K. Banura," Performance Comparison of Gradient Mask Texture Based Image Retrieval Techniques using Walsh, Haar and Kekre Transforms with Image Maps", International Conference on Technology Systems and Management,No. 3, 2011
  15. Sudeep D. Thepade, Pritam H. Patil, ''Novel Video Keyframe Extraction Technique using Thepade's Transform Error Vector Rotation Algorithm Cosine Transform and Assorted Similarity Measures'' Futuristic Trends in Computational analysis and Knowledge management 25th-27yh Feb 2015.
  16. Sudeep D. Thepade, Pritam H. Patil, ''Novel Video Keyframe Extraction using KPE Vector Quantization with Assorted Similarity Measures in RGB and LUV Color Spaces'', International Conference on Industrial Instrumentation and Control (ICIC) 28th -30th may 2015.
  17. Sudeep D. Thepade, Pritam H. Patil, ''Video Key Frame Identification using Thepade's Transform Error Vector Rotation Algorithm with Haar Transform and Assorted Similarity Measures'', International Conference on Communication and Signal Processing 6th-8th April 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Key frame video summarization vector quantization hademard

Powered by PhDFocusTM