Unsupervised texture feature classification based on ...

Unsupervised Texture Feature Classification Based on Cuckoo Search and Relief Algorithm a

Mingwei Wanga, Youchuan Wana, Zhiwei Yeb, Maolin Chena School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China b School of Computer Science, Hubei University of Technology, Wuhan 430068, China ABSTRACT

Gabor filters and K-means algorithm are two commonly used texture analysis methods. However, the texture feature vector has a high dimension by using Gabor filters, which will influence the operating efficiency. Meanwhile, K-means algorithm is affected by the initial clustering centers, and it may lead to the decrease of classification accuracy. Hence, Relief algorithm is applied to make a feature selection for Gabor texture feature, and obtain a suitable texture feature sunset. Furthermore, cuckoo search is used to optimize the clustering center of K-means algorithm, and enhance the accuracy and efficiency of texture recognition. Experimental results demonstrate the effectiveness of the proposed method. Keywords: Texture feature classification; Gabor filter; K-means algorithm; Relief algorithm; Cuckoo search

1. INTRODUCTION Texture [1] is an important characteristic of the appearance of objects in natural scenes and is a powerful visual cue, used by both humans and machines in describing and recognizing objects of the real world. Texture image classification [2] is a vital topic in machine vision and image analysis, which is to identify a texture sample as one of several possible classes with a reliable texture classifier, and playing a very important role in a wide range of applications. In the real world, there are kinds of texture due to changes in orientation, scale or other visual appearance, as a result, various feature extraction and classification techniques have been presented in the past for the purpose of texture analysis, such as, Markov random filed (MRF), fractal theory, gray level co-occurrence matrix (GLCM), local binary pattern (LBP) and so on [3-6]. As a consequence, their performance is satisfied for the analysis of micro-textures only. Moreover, they are single resolution techniques, resulting in poor performance for texture analysis. More recently, methods based on multi-resolution or multi-channel analysis such as wavelet transform (WT) and Gabor filters have gained a lot of attention for texture analysis such as texture classification or texture recognition [7-8]. The process of feature extraction using Gabor functions is motivated by the fact that, these filters can be considered as orientation and scale tunable detectors. Basically, Gabor filters are a group of wavelets, with each wavelet capturing energy at a specific frequency and at a specific orientation or direction. There are several approaches to texture classification and segmentation, both supervised and unsupervised, using banks of Gabor filters with different scale and orientation tuning [9-11]. However, several of scale and orientation features include certain of redundant information, which will reduce the operating efficiency of texture recognition. As one of the filter based feature selection methods, the Relief algorithm is an effective, simple, and widely used approach to feature weight estimation [12]. A great deal of scientific research proved that Relief algorithm could avoid the curse of dimensionality, and improve the recognition efficiency on some extent [13-14]. On the other hand, as a commonly used texture recognition method, clustering analysis could be utilized to make classification for different texture areas. Among them, K-means algorithm is widely used because of its simple operation process [15]. However, the results of K-means algorithm mainly depend on the initial clustering center, and easily drop into the local optimum, which may not stably obtain the optimal solution [16]. Moreover, the optimization of clustering center is a combinatorial optimization problem, which can be solved by swarm intelligence algorithm. Cuckoo search (CS) is a newly proposed stochastic global swarm intelligence algorithm [17]. Nowadays, CS has been widely used in diverse applications, and puts up good optimization ability. So, in the paper, a texture feature recognition technique is proposed by using Gabor filters and Relief algorithm to obtain the optimal feature subset; and then, CS is utilized to optimize the clustering center of K-means algorithm. The experimental results demonstrate the effectiveness of the proposed method.

Ninth International Conference on Digital Image Processing (ICDIP 2017), edited by Charles M. Falco, Xudong Jiang Proc. of SPIE Vol. 10420, 104201G · © 2017 SPIE · CCC code: 0277-786X/17/$18 · doi: 10.1117/12.2281563

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2. BASIC PRINCIPLE OF CUCKOO SEARCH Cuckoo Search (CS) is an evolutionary algorithm proposed by Yang and Deb in 2009 [17]. This evolutionary algorithm is a search strategy model on brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds. If a host bird discovers the eggs are not their own, they will either throw these alien eggs away or simply abandon its nest and build a new nest elsewhere. The better new solution will take place of the solution which is relatively worse in the nest. For simplicity, only three idealized rules are used to describe the CS algorithm as follows. 

Each cuckoo lays one egg at a time, and dumps it in a randomly chosen nest.



The best nests with high quality of eggs (solutions) will carry over to the next generations.



The number of available host nests is fixed, and a host can discover an alien egg with a probability Pa 0,1 . The host bird can either throw the egg away or abandon the nest so as to build a completely new nest in a new location.

In order to generate the new solutions x

 t 1

, call the cuckoo i, a Lévy flight can be defined as the following Eq.(1).

xi 

t 1

 xi      Levy    t

(1)

where   0 is the step size which should be connected with the solve space. The product ⊕ means entry-wise multiplications. This entry-wise product is similar to those used in PSO, the random walk via Lévy flight is more efficient in searching the solve space, and its step length is much longer in the long run. The Lévy flight essentially provides a random walk while the random step length is drawn from a Lévy distribution, which has an infinite variance with an infinite mean:

Levy ~ u  t  1    3

(2)

Here, the consecutive steps of a cuckoo essentially from a random walk process which obeys a power-law step-length distribution with a heavy tail. However, a large proportion of the new solutions may be generated by extensive randomization and whose locations may be far from the current best solution; this will make sure the algorithm will not fall into a local optimum.

3. METHODS 3.1 GABOR FILTERS A Gabor function is defined as a harmonic oscillator, which is complex sinusoidal plane wave of some frequency and orientation within a Gaussian envelope. Thus, it can be stated that Gabor function is the product of a Gaussian function and a complex sinusoid. Moreover, 2-D Gabor filter is an oriented sinusoidal grating modulated by a 2-D Gaussian function, with a modulation frequency ‘W’, and is given in Eq. (3).  1 g( x, y )    2 x  y 

 1  x2 y 2     exp    2  2   2jW ( x cos   y sin )    2  x  y       

(3)

The Gabor filtered output of an image f ( x, y) is obtained by the convolution of the image with the Gabor function G( x, y) . The parameters of a Gabor filter are the modulation frequency W, the orientation parameter θ and the scale σ of the Gaussian function. Hence, Gabor wavelet is usually composed by some Gabor filters with different modulation frequency, orientation and scale. 3.2 RELIEF ALGORITHM As the concept of Relief algorithm, Hypothesis–Margin concepts are used to evaluate the classification capacity of the feature dimension. Hypothesis–Margin is the maximum distance that the decision-making area can move while maintaining the same classification in identical samples; it is defined as follows: 1  x  M ( x)  x  H ( x)  2 where H(x) and M(x) are nearest-neighbor sample points with the same class and different class, respectively. 


(4)

The key idea of the original Relief algorithms is to estimate the quality of features that have weights greater than the threshold using the difference of features value between a given sample and the nearest sample. As the extension of Relief algorithm, Relief-F algorithm is used to solve the multi-class problem, and effectively reduces the data dimension. In the Relief-F algorithm, the weights are calculated as following: k

p( x)

W fi  W fi 1 



c  class ( x )

 diff f ( x, M ( x)) 1  p(class( x)) j 1

m k

k



diff f ( x, H ( x))

(5)

m k

j 1

where p() is the probability, k is an instance selected randomly in every class, and c is a class that is different from class().

4. RESULTS The proposed method is implemented by the language of MATLAB 2014b on a personal computer with a 2.30 GHz CPU, 8.00G RAM under Windows 8 system. To verify the performance of the proposed texture feature classification method, the commonly used GLCM and LBP method are utilized in this part. Moreover, in order to reflect the property of different algorithms, particle swarm optimization (PSO) [18] is also used to make a further comparison. The classification results of 2 test images have been shown in the following.

:H'9

--..---1, K1.

:r

(a)

(b)

(d)

(c)

(e)

',.

Figure 1. Original image and recognized results of TI1: (a) Original image (b) Recognized result of GLCM (c) Recognized result of LBP (d) Recognized result of K-means method optimized by PSO (e) Recognized result of the proposed method

Yi

a'e ti. i .y! r

v*if ^"

,

4' *I

-y! ;,

_ij

;

=ss-..-;±

(a)

(b)

(c)


(d)

(e)

Figure 2. Original image and recognized results of TI2: (a) Original image (b) Recognized result of GLCM (c) Recognized result of LBP (d) Recognized result of K-means method optimized by PSO (e) Recognized result of the proposed method

Table 1 Classification and CPU time for different techniques

Image

Meas.

GLCM

LBP

PSO

Proposed

TI1

Accuracy(%)

41.0537

40.8113

96.6120

96.6776

Time(s)

1522.5013

1.5074

1.0995

1.2246

Accuracy(%)

50.9726

53.8947

76.1170

95.1333

Time(s)

506.6867

1.2311

0.8387

0.9997

TI2

As it is shown in these experiment results, it is clearly that the proposed method makes an accurate identification for each pixel of the texture image. The classification accuracy of the proposed method has a distinctly advantage comparing with other texture feature classification methods. Although PSO optimized technique has faster convergence speed comparing with the proposed method, the difference is less than 0.16s. However, the fitness value is obviously worse; the maximum difference is over 21% for TI2 image. As it is shown in Figures 1-2, the classification results have a certain misclassification for different texture features by using the commonly used classification techniques; for both TI1 and TI2 images, GLCM and LBP methods could not make an exact identification for the details of different texture features, and it is not continuous at the edge section for PSO optimized method. For the proposed method, the edge section basically agrees well with the original image, and each texture feature has been identified on some extents. Moreover, the dimension of texture feature number is greatly reduced, which obviously increase the efficiency of classification. In all, the proposed method is a robust and real-time approach for texture feature classification.

5. CONCLUSIONS In sum, a texture feature classification method based on Relief algorithm and optimized with cuckoo search (CS) is detailed. 2 texture images from public texture database are used to make an evaluation for the proposed method. Results are compared with some commonly used classification techniques such as gray level co-occurrence matrix (GLCM) and local binary pattern (LBP). It is revealed that the proposed method has a better performance; the classification result is satisfied. On the other hand, particle swarm optimization (PSO) is also utilized to make a comparison of optimization ability with CS. In general, it is observed that swarm intelligence algorithm can be well used to complete the task of texture feature classification, and CS has a better performance, which could make accuracy identification for each texture feature; especially at edge part. In sum, the proposed method is able to keep a good balance on the efficiency and classification result, which makes it more suitable for some texture feature classification applications.

ACKNOWLEDGMENTS This work is funded by the National Science & Technology Pillar Program under Grant No. 2014BAL05B07, and the National Natural Science Foundation of China under Grant No. 61301278.


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