## 2 Related Work  
Currently, image extraction methods mainly include edge detection, threshold extraction [13; 14], the clustering algorithm [15; 16], saliency detection [17], and semantic segmentation [18]. Image extraction based on edge detection preserves the edge information of the input image by calculating the derivative between different pixels [19]. However, the algorithm is affected by noise, which might be misjudged as a boundary, thus reducing the edge position accuracy; this is a general disadvantage of the above methods. Threshold extraction, one regional extraction technique, divides pixels into several categories, with the advantage of a low computational complexity [20]. However, this algorithm is applicable to images in which the foreground and background are in different grayscale ranges; the cluster algorithm divides similar features of color space information into a specific group [21]. The saliency detection algorithm extracts the region of the foreground by simulating the visual characteristics of human beings. Considering the characteristics of a high computational complexity, the algorithm may not be applied to real-time applications [22]. Semantic segmentation describes the process of associating each pixel of an image with a class label [23]. The algorithm, based on semantic segmentation, has a high accuracy, but requires a large number of wildlife monitoring images to be marked, resulting in high labor costs. Meanwhile, the high computational complexity of the algorithm makes it unsuitable for WMSNs. It is difficult to extract wildlife images due to complex backgrounds and uneven illumination, which varie significantly in non-uniform illumination in different seasons. Due to the disadvantages of the above extraction methods, [PERSON] et al. [24] proposed an edge detection algorithm for the pixel detection of wildlife images captured by sensor nodes through the calculation of the gray threshold and gradient amplitude. To enhance the accuracy of edge detection, [PERSON] et al. [25] defined an ideology of saliency detection by introducing a positional saliency map. Combined with the edge detection method to extract the images captured by the wireless sensors, this method optimizes the extraction effect and realizes the extraction of wildlife images. [PERSON] et al. [26] segmented multi-colored wildlife images into watershed regions based on watershed transformation. Then, they used the traditional mean-shift algorithm to cluster the watershed regions, which preserves the edge information and effectively suppresses the occurrence of over-segmentation; nevertheless, the traditional mean-shift method based on color space information cannot accurately segment images captured in a complex environment, because it cannot consider the texture parameters in the foreground and background regions. The traditional mean-shift image segmentation algorithm was improved by [PERSON] et al. [27] in 2018. This method combines texture parameters with color space information and then uses edge-preserving filtering to preserve as much edge information as possible, thus using the mean-shift algorithm to achieve superior image extraction. The algorithm is successful in image extraction within a certain range, but a fixed bandwidth must be set manually, which is not suitable for the real-time extraction of images, and the high computational complexity of the algorithm makes it unsuitable for use in WMSNs with a limited power consumption. We try to utilize the adaptive mean-shift algorithm combined with color information and texture parameters to extract the WMSN wildlife monitoring images.