## 3 Proposed Method
### Saliency Cuts Using Local Adaptive Thresholding
#### 3.2.1 Local Adaptive and Global Thresholding  
A number of thresholding techniques have been proposed using global and local techniques. Global techniques assign one threshold to the entire image, whereas local adaptive thresholding techniques assign a varying threshold value to each pixel/region determined by neighboring pixels.  
Global thresholding techniques are very fast and yield reliable results for typical images. Many popular global thresholding techniques use different approaches, such as the fixed thresholding technique, to perform binarization with respect to a specified threshold value. [PERSON] et al. [17] used global fixed thresholding to binarize saliency map images. This approach works well when the background and foreground intensities are clearly distinct and uniform throughout the image. [PERSON] et al. [16] proposed a saliency map binarization technique using the Otsu algorithm to calculate adaptive triple thresholding. The Otsu algorithm is an outstanding automatic thresholding algorithm for image segmentation, thanks to its simplicity and good performance. On the basis of clustering, the Otsu algorithm automatically obtains an optimal threshold from the gray histogram of an original gray image and separates it into the foreground and background. For many years, binarization of natural images has been performed on the basis of global thresholding approaches, which are more appropriate for uniformly illuminated images than diversely illuminated images. Global binarization approaches are more adequate for images with uniform contrast distribution of background and foreground, such as single-object or bi-modal histogram images. However, in complex natural images that contain considerable background noise or variations in contrast and illumination, many pixels cannot be easily classified as foreground or background. In such cases, binarization with global thresholding is not a suitable option.  
Local thresholding techniques apply a unique threshold value to a single-pixel or a certain region. The local threshold value can be calculated using different information contained in the given image. This is also known as dynamic thresholding and can be divided into different approaches, such as the water flow model, background subtraction, mean and standard deviation of pixel values, illumination model, and local image contrast. Although local adaptive thresholding approaches generally achieve better results, they often rely on individual parameters and require a substantially higher computational cost than global thresholding. In this paper, to reduce the computational cost, we used adaptive thresholding based on integral images, which is explained in the next sub-section. [PERSON] [44] proposed fast-adaptive thresholding, which calculates the moving average of the pixels last seen to be the local threshold. [PERSON] simply extended [PERSON]'s method by using integral images to provide a better representation of the surrounding pixels than the moving average by sacrificing one additional iteration through an image [46]. This method is clear and straightforward. Both [PERSON]'s and [PERSON]'s methods focus on the application of document images. These methods can also be applied to natural scene images with some improvements.