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August & September
2019, 12(4&5): 1265-1279.
doi: 10.3934/dcdss.2019087
A new text information extraction algorithm of video image under multimedia environment
| 1. | Qujing Power Supply Bureau, Yunnan Power Grid Co., Ltd., Qujing 655600, China |
| 2. | Dept. of Mathematics and Statistics, Winona State University, Winona, MN 55987, USA |
At present, under the multimedia environment, the accuracy of the text information extraction of video images is poor. Moreover, the extraction process is complicated. In this paper, a text information extraction algorithm for video image based on time adaptive model is proposed. Using Sobel edge detection operator, text edge of the image is extracted. Using OSTU algorithm. the adaptive threshold is obtained, to make global binary processing smooth. Through selective masking smoothing, some irrelevant background is then filtered out. By morphological method, the text area is then merged. Finally, using the connected domain, the text area is extracted, to get the text block, and obtain the text information of the video image. On this basis, according to the color feature of text in video images, through user interaction, the learning process of color online machine based on the generated time and with adaptive model can be obtained to detect the same caption in the video sequence, so as to realize the text information extraction algorithm for video image in multimedia environment. The experimental results show that the proposed algorithm can accurately extract the text information, and the speed of extraction is faster.
Mathematics Subject Classification:
14C35.
Citation:
Xiaohong Zhu, Lihe Zhou, Zili Yang, Joyati Debnath. A new text information extraction algorithm of video image under multimedia environment. Discrete & Continuous Dynamical Systems - S,
2019, 12
(4&5)
: 1265-1279.
doi: 10.3934/dcdss.2019087
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J. Patrick and M. Li, High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge, Journal of the American Medical Informatics Association, 17 (2010), 524-527. Google Scholar |
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S. E. A. Raza, Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain, 7, 2015. Google Scholar |
| [19] |
J. Sun, J. Pang and Z. Zhang, Recognition of vehicle license plate locating based on color feature and improved canny operator, Journal of Jilin University (Science Edition), 693-697. Google Scholar |
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Z. X., W. P. and C. C., Waterbody information extraction from remote-sensing images after disasters based on spectral information and characteristic knowledge, 1404-1422. Google Scholar |
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L. I. Yue-Jie, Specific text in natural scene image optimization identification research and simulation, Computer Simulation, 357-360. Google Scholar |
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D. Zhang, J. Guo, X. Lei and C. Zhu, Note: Sound recovery from video using svd-based information extraction., Review of Scientific Instruments, 87 (2016), 516-198. Google Scholar |
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J. Zhang, W. Geng, L. Zhuo, Q. Tian and Y. Cao, Multiscale target extraction using a spectral saliency map for a hyperspectral image, Applied Optics, 55 (2016), 8089. Google Scholar |
show all references
References:
| [1] |
R. Afshari, B. S. Gildeh and M. Sarmad, Fuzzy multiple deferred state attribute sampling plan in the presence of inspection errors, Journal of Intelligent & Fuzzy Systems, 33 (2017), 503-514. Google Scholar |
| [2] |
F. Ali, E. K. Kim and Y. G. Kim, Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system, Applied Intelligence, 42 (2015), 481-500. Google Scholar |
| [3] |
B. C. Battaglia Onofrio Rosario—Di Paola, K-means clustering to study how student reasoning lines can be modified by a learning activity based on feynman's unifying approach., Eurasia Journal of Mathematics Science & Technology Education, 13 (2017), 2005-2038. Google Scholar |
| [4] |
A. Biniaz, P. Bose, A. Maheshwari and M. Smid,
Packing plane perfect matchings into a point set, Discrete Mathematics and Theoretical Computer Science, 17 (2015), 119-142.
|
| [5] |
G. Brinkmann, S. Dantas, C. M. H. D. Figueiredo, M. Preissmann and D. Sasaki,
Snarks with total chromatic number 5, Discrete Mathematics & Theoretical Computer Science, 17 (2015), 369-382.
|
| [6] |
C. Cardellino, L. A. Alemany, S. Villata and E. Cabrio, Improvements in information extraction in legal text by active learning, Ai Magazine, 18 (2015), 65-79. Google Scholar |
| [7] |
C. Chen and J. Shi, Chinese local government's behavior in land supply in the context of housing market macro-control, Journal of Interdisciplinary Mathematics, 20 (2017), 1289-1306. Google Scholar |
| [8] |
R. Fagin, B. Kimelfeld, F. Reiss and S. Vansummeren, Document spanners: A formal approach to information extraction, Journal of the Acm, 62 (2015), Art. 12, 51 pp.
doi: 10.1145/2699442. |
| [9] |
W. Gao and W. Wang,
A tight neighborhood union condition on fractional $(g,f,n',m)$-critical deleted graphs, Colloquium Mathematicum, 149 (2017), 291-298.
doi: 10.4064/cm6959-8-2016. |
| [10] |
W. Gao, L. Zhu, Y. Guo and K. Wang, Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent & Fuzzy Systems, 33 (2017), 3153-3163. Google Scholar |
| [11] |
G. H., L. J. and Y. Y., Automated road information extraction from mobile laser scanning data, IEEE Transactions on Intelligent Transportation Systems, 25 (2015), 194-205. Google Scholar |
| [12] |
C. C. Hua, J. Feng, L. I. Xue and Y. B. Guo, A method of signal extraction in especial backscatter ionogram, Journal of China Academy of Electronics & Information Technology, 43-48. Google Scholar |
| [13] |
R. Liang, W. Shen, X. X. Li and H. Wang, Bayesian multi-distribution-based discriminative feature extraction for 3d face recognition, Information Sciences, 320 (2015), 406-417. Google Scholar |
| [14] |
S. Linbo and Q. Huayun, Performance of financial expenditure in china's basic science and math education: Panel data analysis based on ccr model and bbc model, EURASIA Journal of Mathematics Science and Technology Education, 13 (2017), 5217-5224. Google Scholar |
| [15] |
H. Liu, Y. Wang, Y. Cai, L. Ma, X. Xing and W. Fan, Tongbo gold ore granite zircon hf isotopic characteristics of spectral image feature extraction, Bulletin of Science and Technology, 37 (2013), 30-34. Google Scholar |
| [16] |
T. Otake, N. Itoh, M. Ohata and N. Hanari, Optimization of microwave-assisted extraction for the determination of organic flame retardants in acrylonitrile butadiene styrene, Analytical Letters, 48 (2015), 2319-2328. Google Scholar |
| [17] |
J. Patrick and M. Li, High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge, Journal of the American Medical Informatics Association, 17 (2010), 524-527. Google Scholar |
| [18] |
S. E. A. Raza, Registration of thermal and visible light images of diseased plants using silhouette extraction in the wavelet domain, 7, 2015. Google Scholar |
| [19] |
J. Sun, J. Pang and Z. Zhang, Recognition of vehicle license plate locating based on color feature and improved canny operator, Journal of Jilin University (Science Edition), 693-697. Google Scholar |
| [20] |
Z. X., W. P. and C. C., Waterbody information extraction from remote-sensing images after disasters based on spectral information and characteristic knowledge, 1404-1422. Google Scholar |
| [21] |
L. I. Yue-Jie, Specific text in natural scene image optimization identification research and simulation, Computer Simulation, 357-360. Google Scholar |
| [22] |
D. Zhang, J. Guo, X. Lei and C. Zhu, Note: Sound recovery from video using svd-based information extraction., Review of Scientific Instruments, 87 (2016), 516-198. Google Scholar |
| [23] |
J. Zhang, W. Geng, L. Zhuo, Q. Tian and Y. Cao, Multiscale target extraction using a spectral saliency map for a hyperspectral image, Applied Optics, 55 (2016), 8089. Google Scholar |
Figure 2.
the edge detection results of the proposed method
Figure 3.
Analysis of text location results by the proposed method
Figure 4.
results of text segmentation by different algorithms
Figure 5.
Comparison of the results of text information extraction by different algorithms
Table 1.
Comparison of the running time of text information extraction by different methods of video images
| image | The proposed method/s | Wavelet neural network algorithm /s | Mean-Shift algorithm /s |
| 1 | 2.13 | 3.56 | 4.12 |
| 2 | 2.45 | 4.17 | 4.56 |
| 3 | 2.06 | 3.43 | 4.03 |
| 4 | 2.37 | 3.87 | 4.42 |
| 5 | 2.18 | 3.64 | 4.24 |
| 6 | 2.25 | 3.79 | 4.31 |
| image | The proposed method/s | Wavelet neural network algorithm /s | Mean-Shift algorithm /s |
| 1 | 2.13 | 3.56 | 4.12 |
| 2 | 2.45 | 4.17 | 4.56 |
| 3 | 2.06 | 3.43 | 4.03 |
| 4 | 2.37 | 3.87 | 4.42 |
| 5 | 2.18 | 3.64 | 4.24 |
| 6 | 2.25 | 3.79 | 4.31 |
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