Image Foreground Extraction Based On Multiscale Features

Image foreground,the image object of interest,is the most vital portion for arbitrary image,and its extraction quality is related to the completion quality of subsequent tasks such as content perception,feature extraction and image analysis.However,natural scene images usually contain complex texture information,while the image texture will weaken the color consistency of the foreground or background,generate pseudo edges within foreground,and further result in poor foreground extraction.What's more,since different images generally contain different degrees of texture information,it is difficult to effectively suppress image texture through simple smoothing preprocessing,which easily causes image under or over smoothing.To make up for the shortcomings of traditional foreground extraction algorithms,this thesis constructs an image multiscale edge-preserving smoothing model according to the changing law of image visual effects with the observation distance.Combining image edges and color distributions at different scales,an image foreground extraction model fused with multiscale features is proposed to realize the effective and accurate extraction of image foreground objects.The main work is briefly described as follows:(1)To overcome the negative effect of image texture on foreground extraction,this thesis analyzes the isotropic and anisotropic diffusion mechanisms,compares their advantages and disadvantages,summarizes the ideal conditions of the edge-preserving diffusion kernel function,proposes a new edge-preserving diffusion function,and theoretically proves that this function performs anisotropic diffusion and approximate isotropic diffusion at image edges and textures,respectively,and has good edge preservation while smoothing image textures.Using the diffusion function,an image multiscale edge-preserving smoothing model is established,which can provide image edges and regional color distributions for the subsequent foreground extraction process.(2)To accurately extract foreground objects from smooth components,the Gaussian mixture model is utilized to model the image pattern of foreground and background according to the color cohesion in the same image region and the color difference between different regions.For each smoothing component,the number of image regions is estimated with their intensity histogram,which further improves the estimation accuracy of foreground and background patterns.Combining image edges and foreground pattern,a graph of foreground extraction is constructed,the energy functional of foreground extraction is proposed,and the effective extraction from smoothing component is realized with the aid of alternate optimization of pattern parameter estimation and segmentation.(3)To overcome the negative impact of fixed scale on foreground extraction,this thesis studies the relationship between image observation results and observation distance.Combining image multiscale edge-preserving smoothing and foreground extraction of smooth components,a foreground extraction model fused with multiscale features is proposed.By alternately performing image smoothing and foreground extraction,the extraction results of the original image at different scales can be obtained.To extract the foreground from an appropriate scale,this thesis analyzes the similarity of extracted results on adjacent scales,and designs an iterative termination condition to ensure that the algorithm terminates at an appropriate scale,thereby solving the negative impact of inappropriate scales on foreground extraction.Experiments demonstrate that the edge-preserving diffusion function can not only preserve foreground edges accurately,but also suppress image textures effectively.What's more,the results on the BSD500,CMU and GSC data sets have proved that the proposed model can achieve accurate and effective extraction for foreground objects in natural images.