## 5 Experimental Results and Discussion
### Experiment and Analysis  
We compared the experimental results of our algorithm with three other extraction algorithms, including N-cuts (N-cuts) [41], the aggregating super-pixels (SAS) algorithm [42], and the histogram contrast saliency detection (HCS) algorithm [25]. Experimental samples were selected from wildlife monitoring images of different species with different backgrounds and different light intensities in the Saihan Ula Nature Reserve due to seasonal variations. The comparison results are shown in Figure 11. There are wildlife images in Figure 11(1-3) with diverse background complexity. Figure 11(1) has a simple background and shows an extreme difference between the foreground and background color. Figure 11(2) has a higher background complexity with a similar color between the grass and trees in the background, whereas the image of Figure 11(3) has only a single background, and the shadows region under light conditions is similar to those of the wildlife. There are three typical images of wildlife with different intensities of light in Figure 11(4-6). Figure 11(4,5) show wildlife captured under the normal lighting conditions and weak illumination, in which the overall color of the latter is dark and the details are not obvious; there is a bright and dark mutation area under the condition of non-uniform strong illumination in Figure 11(6). All the experiments were performed using MATLAB (2014b, The MathWorks, Natick, MA, USA, 1984) in a workstation with Intel (R) Core (TM) i5-4590 and 8 GB RAM. The above extraction results show that the method proposed in this paper has a superior performance and that its extraction of wildlife regions is more accurate than those of the other three methods. For example, the proposed algorithm, N-cuts, SAS algorithm, and HCS algorithm have a better effect on the extraction of the images of Figure 11(1,3) with simple backgrounds, compared with the extraction of the image of Figure 11(2) with a higher background complexity and a foreground in which grass and trees are very similar in color with slightly different texture features. Therefore, we proposed a method based on adaptive mean-shift and Hermite transform which could effectively segment the image and obtain satisfactory extraction results, whereas the N-cuts algorithm, SAS algorithm, and the HCS algorithm show problems of over-segmentation and even incorrect segmentation. In the surveillance images of wildlife under different illumination conditions, the SAS algorithm and the HCS algorithm caused over-segmentation in weak illumination due to the influence of the shadows, as shown in Figure 11(5). Under the conditions of non-uniform strong illumination, the N-cuts algorithm caused incorrect segmentation in the head and legs of the wildlife in bright and dark mutations, as shown in Figure 11(6). Figure 11: Vision comparison of wildlife image extraction: (**a**) the original image; (**b**) the extraction results of the proposed algorithm; (**c**) the extraction results of the N-cuts algorithm; (**d**) the extraction results of the aggregating super-pixels (SAS) algorithm; (**e**) the extraction results of the histogram contrast saliency detection (HCS) algorithm; and (**f**) ground-truth. For the six wildlife monitoring images described in Figure 11, the wildlife regions segmented by hand were manually labeled through an image splitter with reference to the true value.