Machine Learning Algorithms And Applications Of Image Foreground And Background Separation

Images are the main carriers of information.With the development of computer technology,using computers to collect,process and identify information contained in image has become a popular research field.Image fore-background separation is a very important part of target detection,tracking and recognition.As the name suggests,the fore-background separation is used to separate interest part in image from background.The requirements for separation results are different in various application scenarios.For example,when making expression packages by using image editing tools such as a Tian Tian Pi Tu,MIX.We need accurate separation.However.we need rough separation in driverless cars and image retrieval.Therefore,there are many kinds of separation algorithms and their processing results are also different.Since last century,scholars have proposed Knock,Bayesian,Grabcut and other algorithms.They are uesd to segment image fore-background.Among them,Grabcut is one of the most widely used and classic algorithms.But it requires human-computer interaction to process image,the algorithm is less efficient and automated.In order to solve this problem,We improve the traditional Grabcut algorithm and make it automotive.The main research work of this dissertation is as follows:Firstly,on the basis of introducing the principle of common machine learning algorithms and analyzing their advantages and disadvantages,the principle of Grabcut algorithm is introduced and experiments are conducted.Then analyzing and summarizing the experimental results are provided.Secondly,we propose an automatic image fore-background separation algorithm based on Lab color space's texture features.The image is divided and converted from RGB to Lab color space,then extracting color and texture features of each image sub-blocks.After selecting seeds for regional growth,in order to improve the over-segmentation caused by regional growth,we use region merging to merge image blocks that are belong to the same region.Compared with the traditional method,the proposed algorithm does not require manual interaction and sample for training.The experimental results show that the proposed algorithm is not satisfied very much when processing complex images with similar color for target and background.However,an image with single background and distinct color difference between the target and the background can be separated quickly and accurately,moreover,the separation results are accord with the subjective perception of human eyes.Finally,aiming at the above-mentioned problems in Grabcut algorithm with poor processing results and with reqirement of human-computer interaction,we propose an automatic Grabcut algorithm based on region growing and merging,wherein separation results using the above method are converted into binary image and processed by morphological method form a Mask,Followed by definition of the area threshold to remove some "noise" region to generate a final Mask and using it to initialize Grabcut,in which the mixed Gaussian model and the graph cut theory algorithm is conbined.The experimental results show that the improved algorithm can automatically and quickly separate the fore-background.