Region-based Image Segmentation Using Shape-Varying Agents

Image segmentation is an important problem in image processing. Existing methods have not solved this problem in some aspects. Alife is a new natural computing method with much good performance. We induce it into image segmentation and propose a novel Alife model Shape-Varying Agent (SVA). The new method combines the advantages of Alife approaches and region growing approaches, achieving good performance on image segmentation. Introduction Image segmentation plays an important role in image processing tasks. In current computer vision, image segmentation is the basement for many other processing. It is helpful for coding, classification, recognition and so on. Research on gray image segmentation has gone through several decades, numerous methods have been proposed. They are broadly divided into four categories: threshold methods, boundary based methods, region based methods and hybrid methods. Threshold techniques use histograms to achieve threshold segmentation. It supposes that all pixels whose values are within a certain range belong to the same class. Adaptive threshold and optimal threshold [1] generally need a few calculations and can be completed very easily. Choosing the right threshold is the key to these methods and neglecting all the spatial information of images is the fatal shortage [8]. Boundary based methods depend on edge detection operators. Such operators assume the pixel values rapid change at the boundary between two regions. The well known gradient operators are Sobel filter [2] and Roberts filter [3]. But the results of edge detection are often discontinuous which must be modified to be closed curves to represent the boundaries. The process is a difficult task and these methods do not cope well with noise [8]. Region based methods depend on region homogeneity. The basic idea is to segment the image into the biggest homogeneous regions as large as possible. Typical algorithms are split-and-merge [4], Morphological watershed [5] and Texture analysis [7]. It take account of the spatial information and relatively robust for noises. However, it is difficult to detect edges and choose an appropriate homogeneity criterion. Expensive computation is another problem for region based method. Hybrid methods generally are mixture of some methods. Recently, Alife has been applied to image segmentation [6] and achieves some cheering results. In this paper, we present a novel method termed region based segmentation by shape-varying agents (RSVA). Traditional agent always is a pixel without structure and individual behaviors. Generally, they combine Alife and region growing methods to get segmentation results. The biggest improvement of RSVA is that each agent has its own shape and never be restricted to a pixel size. Agents in different region may have different shape and size. Therefore, shape-varying agents become “Segmentation Primitives” like “Texture Primitives” to some extent. We can get more spatial information and efficient results by shape-varying agent (SVA). International Conference on Information Science and Computer Applications (ISCA 2013) © 2013. The authors Published by Atlantis Press 50 Model of Shape-Varying Agents There are many methods utilizing agents for segmentation [8,9]. Most of them take a pixel as an agent for region growing. Apparently, they cannot deal with nature image very well (e.g. grass image, leaves image). For example, in a nature image, we just want to know where the grass is, where the tree is or where is the sun. Most of us will not to concern every pieces of grass or every piece of leaves. In these situations, one-pixel agents always are over segment. They cannot solve it very well. In RSVA, shape-varying agents (SVA) which can include more information (like texture primitives) are distributed in every different region of the image; they can grow into any shape in order to typically delegate the features of this region. These SVAs make the image to be texture homogenous and similar ones with identical structure features will be unified to construct the homogenous image region as large as possible. Therefore, it can avoid over segmentation very easily. Individual structure and behavior render shape-vary agent more intelligent than any other agents. On the other hand, we think if an agent finds its proper living environments at some direction, it will move on at this direction to expend its living fields. Whereas, differing from the stochastic strategy of most Alife methods, SVA adopts inertial strategy for agents' movements. The image R is considered as the living environment of SVAs, different subregion Error! Reference source not found. has different living conditions. A SVA suits for the subregion Error! Reference source not found. where it born. } , , { 3 2 1 n i R R R R R R    ,   j i R R  , for every i and j, j i  . SVA is greedy. It is inspired to expand its field as large as possible. The process can be divided into two parts: occupying and unifying. Occupying phase is an outer instruction, including growth, outspread and split. Unifying phase mainly has four instructed regulations and regarded as an intern instruction. More details will be discussed in the following sections. 2.1 Occupying Process This phase mainly solve the segmentation among agents. We take it as outer instruction because it works among different classes of agents. In this phase, SVAs move as subsequent three rules: (1) Growth. The growth rule is the most important rule in RSVA. Now that we call SVA “segmentation primitive”, it must present the features of segments. Similar to “Texture Primitives”, SVA is hypostatic, shaped and local. To design the SVA, the Error! Reference source not found. -radius neighborhood is firstly defined, Definition 2.1 The r-radius neighborhood r t s P N ) , ( of a SVA is , |) | |, max(| | ) , ( { ) , ( r t v s u v u Q N r t s P     } _ ) , ( _ ) , ( size t s P size v u Q  (1) where Error! Reference source not found. is the position of SVA, r is the distance between two SVAs (e.g. r=1 means that the neighbors of agent P(s, t) is its 8 neighbors which has identical shape and identical size). Definition 2.2 Each SVA is designed to represent the features of its neighborhoods. The growth rule is depicted as )) , ( ), , ( ( _ ) , ( _ ) , ( 1 v u Q t s P F size t s P size t s P n n n n    (2)  )) , ( ), , ( ( v u Q t s P F Error! Reference source not found. (3)