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Open AccessArticle

A Novel Extraction Method for Wildlife Monitoring Images with Wireless Multimedia Sensor Networks (WMSNs)

School of Technology, Beijing Forestry University, Beijing 100083, China
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Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(11), 2276; https://doi.org/10.3390/app9112276
Received: 26 April 2019 / Revised: 25 May 2019 / Accepted: 30 May 2019 / Published: 2 June 2019
(This article belongs to the Special Issue Intelligent Processing on Image and Optical Information)
In remote areas, wireless multimedia sensor networks (WMSNs) have limited energy, and the data processing of wildlife monitoring images always suffers from energy consumption limitations. Generally, only part of each wildlife image is valuable. Therefore, the above mentioned issue could be avoided by transmitting the target area. Inspired by this transport strategy, in this paper, we propose an image extraction method with a low computational complexity, which can be adapted to extract the target area (i.e., the animal) and its background area according to the characteristics of the image pixels. Specifically, we first reconstruct a color space model via a CIELUV (LUV) color space framework to extract the color parameters. Next, according to the importance of the Hermite polynomial, a Hermite filter is utilized to extract the texture features, which ensures the accuracy of the split extraction of wildlife images. Then, an adaptive mean-shift algorithm is introduced to cluster texture features and color space information, realizing the extraction of the foreground area in the monitoring image. To verify the performance of the algorithm, a demonstration of the extraction of field-captured wildlife images is presented. Further, we conduct a comparative experiment with N-cuts (N-cuts), the existing aggregating super-pixels (SAS) algorithm, and the histogram contrast saliency detection (HCS) algorithm. A comparison of the results shows that the proposed algorithm for monitoring image target area extraction increased the average pixel accuracy by 11.25%, 5.46%, and 10.39%, respectively; improved the relative limit measurement accuracy by 1.83%, 5.28%, and 12.05%, respectively; and increased the average mean intersection over the union by 7.09%, 14.96%, and 19.14%, respectively. View Full-Text
Keywords: wireless multimedia sensor networks; wildlife monitoring image; extraction; Hermite; adaptive mean-shift wireless multimedia sensor networks; wildlife monitoring image; extraction; Hermite; adaptive mean-shift
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MDPI and ACS Style

Liu, W.; Liu, H.; Wang, Y.; Zheng, X.; Zhang, J. A Novel Extraction Method for Wildlife Monitoring Images with Wireless Multimedia Sensor Networks (WMSNs). Appl. Sci. 2019, 9, 2276. https://doi.org/10.3390/app9112276

AMA Style

Liu W, Liu H, Wang Y, Zheng X, Zhang J. A Novel Extraction Method for Wildlife Monitoring Images with Wireless Multimedia Sensor Networks (WMSNs). Applied Sciences. 2019; 9(11):2276. https://doi.org/10.3390/app9112276

Chicago/Turabian Style

Liu, Wending; Liu, Hanxing; Wang, Yuan; Zheng, Xiaorui; Zhang, Junguo. 2019. "A Novel Extraction Method for Wildlife Monitoring Images with Wireless Multimedia Sensor Networks (WMSNs)" Appl. Sci. 9, no. 11: 2276. https://doi.org/10.3390/app9112276

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