Abstract
Keyframe extraction of videos is useful in many application areas such as video copy detection, retrieval, indexing, summarization. In this paper, we propose a novel shot-based keyframe extraction algorithm. The proposed algorithm is capable of detecting both shots and keyframes of any video efficiently. For extraction of keyframes, frames of video are clustered into shot transitions. These shot transitions of the video are obtained using higher-order color moments and zero-normalized pixel correlation coefficients. In each shot, all the frames are scanned to detect frame with highest standard deviation in that particular shot and chosen as keyframe to that shot. The proposed method is tested on videos of personal interviews with luminaries. Performance of the proposed method is evaluated on the basis of five parameters—recall, figure of merit, detection percentage, accuracy and missing factor. The proposed method is able to detect both abrupt and gradual shot transitions with comparatively less computational complexity. The exhaustive analysis of results shows the sound performance of the proposed method over the methods used in this study.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Guan, G., Wang, Z., Lu, S., Deng, J. D., & Femng, D. D. (2013). Keypoint-based keyframe selection. IEEE Transactions on Circuit System Video Technology, 23(4), 729–734.
Mohanta, Partha Pratim, Saha, Sanjoy Kumar, & Chanda, Bhabatosh. (2012). A model-based shot boundary detection using frame transition parameters. IEEE Transactions on Multimedia, 14(1), 223–233.
Birinci, M., Kiranyaz, S. (2014). A perceptual scheme for fully automatic video shot boundary detection. 29(3), 410–423.
Tavassolipour, M., Karimian, M., & Kasaei, S. (2014). Event Detection and Summarization in Soccer Videos Using Bayesian Network and Copula. IEEE Transactions on Circuits and Systems for Video Technology, 24(2), 291–304.
Lu, Z. M., & Shi, Y. (2013). Fast video shot boundary detection based on SVD and pattern matching. IEEE Transactions on Image Processing, 22(12), 5136–5145.
Ayadi, T., Hamdani, M., Alimi, T. M., & Adel, M. (2013). Movie scenes detection with MIGSOM based on shots semisupervised clustering. Neural Computing and Applications, 22(7), 1387–1396.
Loukas, C., Nikiteas, N., Schizas, D., Georgiou, E. (2016). Shot boundary detection in endoscopic surgery videos using a variational Bayesian framework. 11(11), 1937–1949.
Dutta, D., Saha, S. K., & Chanda, B. (2016). A shot detection technique using linear regression of shot transition patterns. Multimedia Tools and Applications, 75(1), 93–113.
Jadhava, P. S., & Jadhav, D. S. (2015). Video summarization using higher order color moments. In Proceedings of the International Conference on Advanced Computing Technologies and Applications (ICACTA) (Vol. 45, pp. 275–281).
Sheena, C. V., Narayanan, N. K. (2015). Key-frame extraction by analysis of histograms of video keyframes using statistical methods, In Proceedings of the 4th International Conference on Eco-friendly Computing and Communication Systems (Vol. 70, pp. 36–40).
Gonzalez-Diaz, I., Martinaz-Cortes, T., Gallardo-Antolin, A., & Diaz-de-Maria, F. (2015). Temporal segmentation and keyframe selection methods for user-generated video search-based annotation. Expert Systems with Applications, 42, 488–502.
Hannane, R., Elboushaki, A., Afdel, K., Naghabhushan, P., Javed, M. (2016). An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram. International Journal of Multimedia Information Retrieval. 10.1007%2Fs13735-016-0095-6.
Thakre, K. S., Rajurkar, A. M., Manthalkar, R. R. (2015). Video partitioning and secured keyframe extraction of MPEG video. In Proceedings of the International Conference on Information Security & Privacy (ICISP2015), Nagpur, India, Procedia Computer Science, (Vol. 45, pp. 275–281).
Dang, C., & Radha, H. (2015). RPCA_KFE: Key frame extraction for video using robust principal component analysis. IEEE Transactions on Image Processing, 24(11), 1–12.
Lee. Virtual Dub home page. http://www.virtualdub.org/index.html.
Poornima, K., & Kanchana, R. (2012). A method to align images using image segmentation. International Journal of Soft Computing and Engineering, 2(1), 294–298.
Khare, M., Srivastasava, R. K., Khare, A. (2015). Moving object segmentation in daubechies complex wavelet domain. Journal of Signal, Image and Video Processing, 9(3), 635–650.
Shaker, I. F., Abd-Elrahman, A., Abdel-Gawad, A. K., Sherief, M. A. (2011). Building extraction from high resolution space images in high density residential areas in the Great Cairo region. Remote Sensing, 3, 781–791.
Martn, R. V., & Bandera, A. (2013). Spatio-temporal feature-based keyframe detection from video shots using spectral clustering. Pattern Recognition Letters, 34(7), 770–779.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Reddy Mounika, B., Prakash, O., Khare, A. (2019). Fusion of Zero-Normalized Pixel Correlation Coefficient and Higher-Order Color Moments for Keyframe Extraction. In: Khare, A., Tiwary, U., Sethi, I., Singh, N. (eds) Recent Trends in Communication, Computing, and Electronics. Lecture Notes in Electrical Engineering, vol 524. Springer, Singapore. https://doi.org/10.1007/978-981-13-2685-1_34
Download citation
DOI: https://doi.org/10.1007/978-981-13-2685-1_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2684-4
Online ISBN: 978-981-13-2685-1
eBook Packages: EngineeringEngineering (R0)