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image classification with ant colony based support vector machine

Image Classification with Ant Colony Based Support Vector Machine

ZHAO Baoyong1,2, QI Yingjian3

1. School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China

2. Key Laboratory of Advanced Control of Iron and Steel Process, University of Science and Technology Beijing,

Beijing 100083, P.R. China.

E-mail: zbyfirst@https://www.wendangku.net/doc/0c13294029.html,

3. Science School, Communication University of China, Beijing 100024, P.R. China

E-mail: qiyingjian@https://www.wendangku.net/doc/0c13294029.html,

Abstract: Natural image classification is an important task. SIFT descriptors and bag-of-visterms (BOV) method have achieved very good results based on local image representation. Many studies use the support vector machine to classify and identify the image category after finished representation of the image. However, due to support vector machine (SVM) its own characteristics, it shows inflexible and less slow convergence rate. The selection of parameters influenced the results for the algorithm seriously. Therefore, this paper try to improve the image recognition performance by support vector machine algorithm based on ant colony algorithm. The method adopt dense SIFT descriptors and BOV method to obtain the image representation. In recognition step, we use the support vector machine as a classifier but ant colony optimization method is used to selects kernel function parameter and soft margin constant C penalty parameter. Experiment results show that this solution determined the parameter automatically without trial and error and improved performance on natural image classification tasks. Key Words: Scale Invariance Feature Transform, Bag-of-Visterms, Colony Algorithm, Support Vector Machine

1Introduction

Classification of natural image automatically is one of the significant research fields in computer vision. Effective image description is the base of the correct image classification. And then how to discover the objects and use the objects distribution to perform scene classification is regarded as in an unsupervised manner. Currently, the method based on semantic analysis represents the image mainly through the use of intermediate semantic (or Topic) [1, 2]. Each image can be expressed by a number of Topics which based on the probability distribution of the so called visual words (visual visterms).

Recently, Bag-of-visterms (BOV) becomes an important image content representation method [3]. Imitation the idea of text classification, the semantic information is exhibition of the words distribution. The image can be seen as composed by the visual words document, and then the visual words used to classify the image. However, BOV's problem is that the image is difficult to define specific meaningful visual words, because it needs to transform the image characteristics into the visual terms effectively. A lot of work in this area has done: Some used the method that found the dense sampling local area and extracted the feature vector in the region to express the image content, thus greatly improving the performance of the visual vocabulary [4, 5]. Some detected the interest image region, and then extracted the SIFT descriptor in the region as a feature vector [6, 7]. Clustering and classification of vocabulary commonly used k-means clustering method, but improved algorithm combined with other optimized cluster method to achieve improvements on the classifier has continued [8-10].

Support Vector Machine (SVM) is a new learning machine developed on the basis of structural risk *This work is supported by the project from Beijing Key Discipline Development Program (No. XK100080537) minimization principle [11]. It aimed at finding compromise between complexity of model and the best learning ability in the based of limited sample information for good generalization ability. Support vector machine is based on solid statistical learning theory and have theoretical completeness, but there are still some problems on the application, one of the typical problems is the choice of model parameters. There is no uniform model selection standards and theory. Penalty factor C and kernel function parameters have a major impact on the classification performance in specific applications [12, 13]. Ant colony algorithm is a new heuristic evolutionary algorithm. It has a strong ability to find better solutions, better robustness, information positive feedback, parallel distributed computing and easy to combine with other heuristic methods [14]. It is a good idea to use ant colony algorithm in support vector machine which provides powerful method for SVM parameter selection problem.

This paper gives a new method that uses the bag of word method in creating an image described, the neighborhood features of the image is described by using 128-dimensional sift descriptor, and then using two-level vector quantization to build vocabulary, while getting semantic description of the image by the pyramid dense sampling. In the image recognition stage, SVM classifier [15] is used in recognition and the parameters of SVM is optimized by using ant colony algorithm [16], and test it in the image database. Experimental results show that the method can automatically determine the SVM parameters and can better improve the performance of the natural image classification.

Proceedings of the 30th Chinese Control Conference July 22-24, 2011, Yantai, China

2 Image Classification Mod el Based on Bag-of

-Visterms

2.1 SIFT Descriptor and BOV Model

Scale Invariant Feature Transform (SIFT) [7] is proposed by David G. Lowe to generate the set of image features. SIFT algorithm extracts the independent eigenvectors for the scale zooming, rotating, brightness variations from the image. The generation algorithm of SIFT feature vector about an image includes a total of Step four.

The first step is the preliminary identification of key positions and the scale by using the scale space extreme detection. After that the critical point location, the low contrast key points and the instability edge response point are removed in order to increase the match steady and increase resistance to noise. The third step is that the direction parameter of each key point is specified by using the gradient direction distribution features of the key point neighboring pixel. At last SIFT feature vector are generated. In the actual calculation process, in order to enhance the robustness of matching, Lowe recommends each key points is described by using 4 × 4 with a total of 16 seed points, such a critical point can be generated 128 data, which eventually form 128-dimensional SIFT feature vector. SIFT feature vector has removed influence from scale change, rotation and other geometric distortion factors. It provides robustness to image clutter, partial visibility, and occlusion. They were designed to have high degree of invariance, and are robust to changes in viewpoint and lighting conditions. Hence it is used as an important descriptor since it is proposed.

There are several different steps in construction feature vector h from an image I by the bag-of-visterms (BOV) [17]. (1) Extract sifts descriptor. We extract the sift descriptor from the selected key points of the original image. :NKXK GXK Z]U SKZNUJY LUX QK_ VUOTZY YGSVROTM OT MKTKXGR . One is the sparse sampling method and the other is the dense sampling method. After key point selection, we can calculate the sift descriptor of each point shows in last section.

(2) Construct vocabulary model. Given an image, we obtain a set of local interest point descriptors. We then quantize each local descriptor into one of a discrete set V of visterms v according to a nearest neighbor rule:

s ()(,)(,){1,,}i i j j v Q s v dist s v dist s v N d 6"Where v N denotes the size of the visterms set. We will call vocabulary the set V of all visterms. The vocabulary is constructed using K-means.

(3) Construct bag-of-visterms. The standard BOV representation of the image is constructed from the local interest point descriptors according to:

1()[(),,()]v

N h I h I h I " With (,,)()v

i i n I v h I Q .

Where (,,)i h I v Q denotes the number of occurrences of visterms i v from vocabulary V in image I . This is the single-level BOV representation of the image. It describes each image using the occurrence information of each

visterms in that image. This vector-space representation of an image contains no information about spatial relationship between visterms.

In brief, we can illustrate the whole process by Figure 1. At first, key points (selected or computed) are automatically detected in the image, then local descriptors are computed over those regions as show in (a). All the descriptors are quantized into visterms as (b), and all occurrences of each specific visterms of the vocabulary in the image are counted to build the BOV representation of the image as (c). 2.2 Support Vector Machine Classifier

SVM methods have proven to be successful in solving machine learning problems in computer vision and text categorization applications, especially those involving large dimensional input spaces. Standard SVM are binary classifiers, which learn a decision function through margin optimization, such that function is large (and positive) for any input which belongs to the target class, and negative otherwise.

Suppose that linear separable sample dataset is (,)i i x y ,

1,2,,,,{1,1}n i n x R y " , where {+1,-1} is class label. The general form of linear discriminative function in n-dimension space is defined as ()g x w x b <. Regulate the function to satisfy ()1g x t so that the minimum class interval equal to 2/w . So maximum of the interval equal to minimum the w and for classify all the sample correctly it should satisfy [()]10,1,2,,i i y w x b i n t <". The super surface is the optimized classification surface. We can solve the problem under the constrained condition by the Lagrange method. The relaxation is added to the condition item for nonlinearity problem and then we can get 01n

i i i i w y x O |and the optimized classifications function

as sgn{()}i i i f y x x b O |<. According to the functional

theory the inner product of feature space ()()i x x ))

3 Improved Image Sampling and Classification

Method

We adopt some improved methods in order to accomplish the natural image classification task. We combined three level pyramid dense sampling and the two-level

quantization method into the image description and then construct the bag-of-visterms representation. At last we employee the ant colony optimization based support vector machine to classify the images in the database.

In the dense sampling step, we obey the following rule in an image. We construct a sequence of grids at resolutions 0,…,L, such that the grid at level l has 2l cells along each dimension. If dimension is equal to 2, then the total cells are

D . Let X and Y are two sets of vectors in a

2-dimension feature space. Let l

X H and l

Y H denote the histograms of X and Y at this resolution, so that ()l

X H i and

()l

Y H i are the numbers of points from X and Y that fall into the ith cell of the grid. It reduces to a standard bag of features when L = 0. We assumed that M denotes the number of cluster center of discrete types for all feature vectors. It is report [1] that the good performance can be obtained just at level 2 in the situation of 200M and 400M . This SUZO\GZKY us to select the level 2 as the decomposition level in our work. We study the experiment as follow and decide the suitable style is MLV1

that combine vocabulary size 100M with 200M . It

gets better performance and increase rate in limited time.

SVM provide the method to search the optimism surface

of the train samples. The basis idea is to find a nonlinear map function that reflects the input data into a high dimension inner multiple space which is accomplished by the kernel function. The main problem is to select the kernel function and adjust the parameters. The two kernel parameter and penalty factor played an important role for improving the model performance. Ant colony method is a good optimization method. It has the advantage of high search efficiency and high adaptable ability without computing the partial derivatives of objective function, decreasing the probability to fall into the local minimum. So we select it as a solution to search the better parameters. The ant is set as a point of the two parameter spaces. We set the following step to realize our idea. At first we support to search in a predefined scope of two parameters with settled distance by m ants. Each ants stay at one point stands for the specific parameter value. They will be calculate the objective function value at the given initial pheromone strength and the increment value. And then each ant move in one or other area by the transfer probability, compute the value of object function again and record the best answer. At last renew the pheromone strength and go into a new round until the precision or the loop count get to the request. If some pheromone becomes more and more strong we will reduce the distance and fine a finest value in a small scope.

4 Experiments and Results

We use the thirteen scene categories dataset introduced by Fei-Fei [18]. This data set contains a total of 3859 images of approx. 60000 pixel resolutions, varying in exact size and XY ratio. The example is as Figure 1.

We get the follow classification performance shows in Table 1. There are really some improvements in the result. But the whole recognition rate is not so satisfied compared

with the best report. We will try to find some other method to improve it.

(a)

(b)

Table 1: classification performance in dataset Vocabulary size SVM (%) Ant-SVM (%)

100 65.1 68.9 200 69.5 72.1 300 70.7 72.3 1000 73.3 78.1 100+200 74.2 78.7

Conclusion

The image classification problem based on the local image representation and improved support vector machine method is studied here. In the recent years, SIFT descriptors and the bag-of-Visterms model has been shown to be a good base for image representation. =K YKRKIZ 9/,: GTJ (5< GY ZNK XKVXKYKTZGZOUT SKZNUJ UL ZNK OSGMK?Y RUIGR LKGZ[XK The classification method is very important for the final performance. On the basis of popular support vector machine, the penalty factor C and kernel function parameters are estimated by the ant colony optimization method. We give the main step of our method. Experiments done on the thirteen classes dataset shows that this method gets improved result. Other discusses of this topic and more experiment will be done next.

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在操作系统里已经自己带了一些桌面图片提供做背景,假如系统里的图片没有你喜欢的,你可以自己把你喜欢的图片来做背景桌面,许多人都是使用有个性的图片来装点自己的电脑屏幕,不过你的图片的尺寸不要小于屏幕尺寸,虽然图片可以在屏幕上使用拉伸来铺满屏幕,效果不会好.操作办法就是去点击浏览,

在你的电脑里找出保存的地方和这个图片,打开, 看看效果怎么样,喜欢了就点击应用

确定以后就保存了这个设置,以后打开电脑就显示你自己设置的这个图片为背景了

当使用了尺寸比较小的图片做背景时候,你不拉伸就是在屏幕的中间一小块,当然这样的小图片也是可以铺满屏幕的,就是使用拉伸,不过拉伸了以后图片的效果家差许多,这就是象素问题了,图片不再是清晰的了,可以看出来颗粒

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