Image Compared by Election Campaign Algorithm
Usually CBIR (Content based image retrieval) is an image retrieval method that exploits the feature of the image as the retrieval index, which is based upon the content, including colors, textures, shapes and distributions of objects in the image. After the feature detecting, the composition of the similarity matching image set is found, then detecting the most matching image still need to be process in the higher level analysis and retrieval. It is a difficult and slow process. So, if we take an opposite approach, detecting the not match image from the similarity matching image set but comparing all the images in the set, it can be more easily to achieve. In this paper, we propose an new image comparison method base on Election Campaign Algorithm, which provide parallel and fast optimum feature detecting, to detect the not match images from the similarity matching image set, then another method would be use to find the most match images. With this method, the image comparison process is fast, the size reduce image set is quickly to be received. Introduction In recent years, with the rapid development of multimedia technology and network technology, more and more rich image resources, text based image retrieval method for text annotation of the subjectivity and complexity of tradition, has been unable to meet Retrieval needs, in this case, image Retrieval Content Based (content based image retrieval (CBIR) technology came into being. The basic idea of content based image retrieval technology is: firstly, image content analysis, automatically or semi automatically extracted from the shape, color and texture features, and then use a similar function to calculate the similarity between the features or evaluation metrics, and finally the most similar image as the search results returned to the user. Stone slab is directly cut from the natural rock blocks. It has the irregular profile and various surface defects, such as the gall stone, holes, cracks and other defects [1] It is difficult to get the best utilization rate of cutting and nesting stone slab, artificial realization is usually the solution of this problem [2, 3] Digital image processing technology provides a technical support to determine such as the minimum external rectangle and maximum inscribed rectangle parallel to the coordinate axes [4] Detecting maximum inscribed rectangle on irregularly shaped is not a tradition image processing method. Also, the optimal layout and cutting of rectangular stone is feasible method to increase utilization rate of a rectangular material [5] Get the maximum inscribed rectangle on irregularly shaped and then process the optimal layout and cutting will be the better utilization rate solution for natural stone production plant [6] In image retrieval, the content of the image is reflected by the characteristic of the image. Image features are divided into two categories, namely, low level visual features and high level semantic features. In these two kinds of features, although the semantic feature based retrieval [7] Compared with the requirements of the use, but due to the current level of development of computer vision and image understanding, and also related to psychology, biological vision and other disciplines, which makes the search for this way is also difficult to achieve. So the research focus of content based image retrieval is mainly focused on the low level visual features. In the retrieval based on low level visual features, color features and description of an image is the most simple and effective features, it not only has rotation invariance and scale invariance, and easier access than other characteristics, so this paper mainly discusses the color feature of the image retrieval based on cable. Include the following aspects: the choice of color space, color quantization, color feature extraction and color similarity measure. 6th International Conference on Management, Education, Information and Control (MEICI 2016) (c) 2016. The authors Published by Atlantis Press 0962 In this paper, we use grey scale feature and position relation to detect the similarity of the comparing images. The author uses the election campaign algorithm to find out the grey scale feature of random position and the position relation of them [8] As an optimization process, the similarity of the images could be detected and the dissimilar images could be easily find out. Between the retrieval based on low level and high level visual features, this process exclude the dissimilar images and reduce the quantity of the images to be detected. The next step, more precise high level retrieval detecting is involved to get the most similar images. Grey Scale Feature of Images Detecting of gray scale feature is not sensitive to the deformation and rotation of the target position in the image, and has good stability [9] Set the image grey scale level is L (0 l) the general l is 255. In order to speed up the computing and improve the real time performance, the grey scale level is mapped to m level, such as m=8, which is divided into 8 levels. So, the grey scale level of point is calculated by the Eq. 1. G r c x m l (1) Corresponding, assuming that the grey scale level of surrounding points at the center point P(r,c) the objective function can be expressed as S(r,c) the sum of levels of points. As is show in Eq. 2. 1,2, i i n S r c sum x m l (2) So, the target localization problem in the process of comparing is transformed into the problem of the candidate target region which is the minimum value. When P(r,c)=S(r,c) I(r,c) is close to 0, said the target and candidate regions are more similar; when P(r,c)>R, R is the evaluation criterion and in this paper we set R=n/2, n is the number of comparing points, said the target and candidate region are dissimilar. Fig. 1 shows the model of target and candidate regions, point (1, 2, 3, 4, 6, 7, 8, 9) is the points surrounding point 5, all together the sum of 9 points gray scale level, such as Eq. 3, is used as the evaluation criterion of the center point. 1,2, ,9 i i S r c sum x m l (3) Figure 1. Surrounding Points of Point 5 After the criterion calculation of center points, the relation of center points is used as the fitness function of the optimization algorithm. Fig. 2 shows the relation model of center points. The numerical center of gravity of all the center points in target image can be found as the solid dot, hollow dots are center points. The relative position relation in target image is used when the same relation is detecting in the candidate image. 6th International Conference on Management, Education, Information and Control (MEICI 2016) (c) 2016. The authors Published by Atlantis Press 0963 Figure 2. Comparing Model of Center Points Election Campaign Algorithm Election campaign algorithm (ECA) is a new optimization algorithm simulating election process [10] Election candidates always pursue the maximum support from voters by means of various election behaviors. They get the overview of their support from the voters according to the sample survey, and then decide what to do on the next move. Solution space is imagined as voters and current solutions are imagined as candidates in ECA. The function value of a feasible solution is named as the prestige of a voter and the function value of a current solution is named as the prestige of a candidate. In ECA, the candidates influence the voters round them, the voter's support is in proportion to the distance between the candidates and the voters. The voter will vote to the candidate which he pefer the most. The sum of location coordinates of every voters supported the candidate powered by its contribution is a new location coordinates, which is named support focus, it is the next position of the candidate. Such computational cycle is done continually until a candidate finds the position of the highest support, which is the global solution of the optimization problems. Framework of the election campaign algorithm is described as the follows: Set the Algorithm Parameters. Main parameters of Election campaign algorithm are: The number of candidates. The number of voters. The number of floating voters. Target accuracy. Generate the Candidates and Calculate the Prestige of Them. Generated the define number of candidates in feasible solution field on the uniform distribution. Use the objective function to calculate the prestige of each candidate. Generate the Voters. The uniform distribution is employed to generate the voters in feasible solution field. Compute the Investigate Mean Square of Candidates. Higher prestige of a candidate, smaller the mean square deviation of local voters, so that ECA is able to converge to local optimization solution rapidly and steadily. The following formula is used to describe the relation of the prestige and the mean square deviation of a candidate.