## 2 Methods
### The Feature Extraction  
There are numerous feature extraction methods that exist in the image processing and computer vision domains. One popular approach to this end is the SIFT feature extraction method that has been extensively used for texture recognition and object identification tasks [27]. In the above-mentioned work, the authors used a standard dataset containing images of patterns and objects. In the context of mosquito classification, the above-mentioned methodology is challenging because it is quite tedious to distinguish mosquitoes considering body pattern as a classification criterion. In this work, we propose a new feature extraction method which accounts for both the color and morphological features of the acquired images of insects. In this feature extraction method, morphological difference between the mosquitoes and insects accounts for the identification of mosquitoes among the other insects. As the first step of feature extraction, the color histogram of each image is extracted. After the color histogram extraction, we segment all images for trunk and leg regions. Upon these, the ratios of the trunk width to the leg length of segmented regions are determined using mathematical formulations and distance calculation algorithms. As the third step in the feature extraction, we perform the truth class labeling for the extracted feature and use it for training the support vector machine classifiers. Figure 2 shows the flow diagram of the feature extraction method used in this research work. For the background-foreground extraction, we used the graph cut algorithm commonly used in the computer vision community. The graph cut is a graph theory-based image processing method which is used for segmentation of images [29]. In order to use the graph cut algorithm for extracting the background or foreground, the algorithm must be trained with foreground or background regions of the image. Therefore, we determined the edges and saliency map of the images. Both the edges and saliency map of the images are taken as the training data for the graph cut algorithm. The saliency map is generated by combining multi scale image features to a feature map which can be used as a tool for rapid scene analysis [30]. The saliency map integrates the orientation, color, and intensity information from an image. Therefore, we extracted the color map, intensity map and orientation map of the input images and integrated them to generate the saliency map. Figure 3 shows the saliency map generated from a sample mosquito image. On a saliency map, the pixels that show the foreground data will possess the highest value. Hence, to identify the foreground, we have defined a couple of thresholds N1 and N2 to determine the foreground from the saliency map. Using trial and error methods, we assigned the values 50 and 30 corresponding to N1 and N2. The pixels that show a value greater than N1% of the maximum pixel value in the saliency map are treated as  
Figure 1: The schematic system architecture diagram of the proposed mosquito classification system. most probable foreground pixels. The pixels that show a value less than N2% of the maximum pixel value in the saliency map are treated as the least probable foreground pixels. The pixels that have a value range between N2% to N1% of the maximum pixel value in the saliency map are considered as moderately probable foreground pixels. However, with the help of the saliency map alone, we cannot determine the foreground pixels accurately. Hence, we also used the edge information of the image for determining the foreground accurately. The edge information from the image is done by applying a Sobel filter in the image [31]. The Sobel filter applies a 2D gradient on the image, so that the rapid changes in a grayscale image along the vertical and horizontal direction can be determined. To determine the foreground pixels from an edge image, we introduced certain thresholds for deciding the foreground and background data. As we did in the saliency map, three thresholds N3, N4 and N5 were fixed.