Texture Image Classification with Improved Weber Local Descriptor ...

Texture Image Classification with Improved Weber Local Descriptor Hassan Dawood, Hussain Dawood, and Ping Guo Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing 100875, China {hasandawod,hussaindawood2002}@yahoo.com, [email protected]

Abstract. Texture features play an important role in image texture classification. Inspired by Weber’s law, Weber Local Descriptor (WLD) has been proposed for image texture classification. Orientation component in Weber Local Descriptor is the gradient of an image, which does not properly represent the local spatial information of an image. In this paper for orientation component, we propose to compute the histogram of gradient instead of the gradient of an image. The gradient of an image is computed, then image is divided in to small spatial regions named as cells and histogram of each cell is obtained. We have tested our proposed scheme on publically available texture datasets named as Brodatz and KTH-TIPS2-a, which shows that our proposed method can achieve significant improvement as compared to the state-of-the-art method like Local Binary Pattern, Local Phase Quantization and Weber Local Descriptor. Keywords: Weber Local descriptor, Histogram of Gradient, Image Texture Classification.

1

Introduction

Texture image classification has been an active research topic in computer vision and image processing. It is used in many applications like object-based image coding [1], image retrieval and remote sensing[2], Medical image analysis and image retrieval [3]. With the increment of the texture image classification applications, plenty of work has been done by researchers in last two decades. For local features number of methods have been proposed. These local descriptors are categorized into two classes that are sparse descriptor and dense descriptor. The dense local descriptor extracts the features pixel by pixel from given image. The typical examples of dense descriptor are Local Binary Pattern (LBP) [4], Local Phase Quantization (LPQ) [5], Weber Local Descriptor (WLD)[6] and Gabor wavelet[7]. The sparse descriptor first detects the interest points in given image then samples a local patch and describes its invariant features. The most popular examples of sparse descriptors are scale invariant feature transform (SIFT) [8] and histogram of oriented gradients (HOG) [9].

Corresponding author.

L. Rutkowski et al. (Eds.): ICAISC 2014, Part I, LNAI 8467, pp. 684–692, 2014. c Springer International Publishing Switzerland 2014

Texture Image Classification with Improved Weber Local Descriptor

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Ojalaet et al. [4] proposed a method for texture image classification named as LBP. This method recognizes the certain local binary patterns, termed as “uniform”. It is the basic property of local image texture. LBP has a drawback of losing global spatial information. A new texture descriptor named as LBP variance (LBPV) has been proposed to characterize the local contrast information into one dimensional LBP histogram. There is no need of quantization and training [10]. A complete modeling of the local binary pattern (LBP) operator is proposed by Guo et al. named as completed-LBP (C LBP) [11] in which local region is represented by its central pixel and a local difference sign-magnitude transform (LDSMT) collectively. The central pixels represent the image gray level which is converted into binary value named as CLBP-Center (C LBPC). The LDSMT have two parts, one is CLBP-Sign (C LBPS) and other is CLBPMagnitude (C LBPM). A Monogenic-LBP (M-LBP) [12] is used to integrate the traditional Local Binary Pattern (LBP) operator with the other two rotation invariant measures: the local phase and the local surface type. These are computed by the 1st-order and 2nd-order Riesz transforms, respectively. Soo and Kang [13] proposed the feature extraction method by using wavelet packet frame decomposition and the Gaussian-mixture-based classifier to assign each pixel to the class. Each subnet of the classifier is modeled by a Gaussian mixture model and each texture image is assigned to the class to which pixels of the image most belong. Zhang et al. [14] proposed a new method to estimate the dominant orientations of textures using Gabor filters, where it’s modified version is used to fit the multi-orientation cases. The discrete wavelet transform is used as a feature extraction tool and nearest neighbor method is used for classification. Some statistical methods that are insensitive to blur, have been used for texture image classification, however these methods are not rotation invariant, like Gabor filtering [7], wavelet frames [15], wavelet transform [16] and co-occurrence matrix method [17]. Weijer and Schmid proposed a blur robust descriptor based on color constancy [19]. Ojansivu et al. [5] proposed a descriptor for texture image classification named as Local Phase Quantization (LPQ), in which shorttime Fourier Transform (STFT) is used to extract the image features. Dawood et al. [19] proposed a method in which they consider the contrast information in spatial domain and the phase information in frequency domain of the image. They have used the joint histogram of the two complementary features, Local Phase Quantization (LPQ) and the contrast of the image. Chen et al. [6] proposed a Weber Local Descriptor (WLD) for texture image classification, which consists of two components: orientation and differential excitation. Where the orientation is the gradient orientation of the current pixel and differential excitation component is the function of the ratio between two terms, one is relative intensity differences and other is intensity of the current pixel. A hybrid approach that combines the WLD with contrast information is proposed by Dawood et al. [20] in which, the histograms of WLD and contrast information are computed independently and then combined to get the robust descriptor. The orientation component in the WLD is the gradient orientation

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of the current pixel. The computed features from orientation component, blur the texture of an image, which leads to misclassification of images. To overcome the aforementioned problem, we have proposed to compute the gradient over an image. Then gradient image is divided into small spatial regions “cells” and the histogram is computed for all small cells. In order to obtain the neighbouring pixel information differential excitation is used with histogram of gradients. Experiments conducted on Brodatz and KTHTIPS2-a datasets show that our proposed method performs well in term of classification as compared to the state-of-the-art feature extraction methods like LBP[4], LPQ [5],and WLD [6] itself. Support Vector Machine (SVM) is used for the classification task. The rest of this paper is organized as follows: In section 2, a brief introduction of Differential excitation and gradients. In section 3, our proposed method is described. The detailed experiments are presented in section 4, and finally we provide the conclusions in section 5.

2

Related Work

In this section, we will briefly review the differential excitation and Histogram of gradient. 2.1

Differential Excitation

Jian [21] stated that the ratio of the increment threshold to the background intensity is a constant which is known as weber law, it can be expressed as ΔI/I = k, where ΔI represents the increment threshold (just noticeable difference for discrimination), I represents the initial stimulus intensity, and k signifies that the proportion on the left side of the equation remains constant despite variations in the I term. The fraction ΔI/I is known as the Weber fraction. In WLD [6], differential excitation ξ(xc ) of a current pixel xc is calculated. The differences between the center point and its neighbors is calculated by using filter f00 . ⎤ ⎡ x1 x2 x3 xs = ⎣ x8 xc x4 ⎦ x7 x6 x5 ⎡

f00

⎤ +1 +1 +1 = ⎣ +1 −8 +1 ⎦ +1 +1 +1 ⎤ 0 0 0 = ⎣ 0 +1 0 ⎦ 0 0 0 ⎡

f01


vs00

=

p−1 i=0

(Δx) =

p−1

(xi − xc )

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(1)

i=0

where xi (i = 0, 1, ..., p−1) denotes the ith neighbour of xc and p is the number of neighbours. Following the hints in Weber’s law, by combining the two filters f00 and f00 , the ratio of the differences to the intensity can be computed. The output vs01 is the original obtained image. Gratio (xc ) =

vs00 vs01

(2)

The differential of the current pixel ξ(xc ) is computed as

p−1 00 xi − xc vs ξ(xc ) = arctan 01 = arctan ( ) vs xc i=0

(3)

If the intensities of the neighbouring pixels is smaller than the current pixel, the value of the differential excitation will be negative. From this, we can see that instead of using the absolute value of ξ(x) the more discriminating information is preserved. Intuitively, if ξ(x) is positive, then the surroundings are lighter than the current pixel. if ξ(x) is negative, it simulates the case that the surroundings are darker than the current pixel. ξ(xc ) is defined in the range of [−π/2, π/2]. 2.2

Histogram of Gradient

By computing the gradient of an image, we can observe that the image is changing rapidly. Gradient of an image has two kind of information, one is magnitude and other is direction of the gradient. Magnitude gives the information of how rapidly the image is changing and direction of the gradient tells us direction image is changing more rapidly. The gradient of the image f (x, y) at location (x, y) is defined as ∂f

Gx ∂x ∇f = (4) = ∂f Gy ∂y We can detect the edges in image by computing the magnitude of the vector, ∇f = mag(∇f ) =

G2x G2y

1/2

And the direction of gradient can be computed as Gy α(x, y) = tan−1 Gx

(5)

(6)

Where the angle is measured with respect to x-axis and the edge direction at (x, y) is perpendicular to the direction of the gradient vector at the point.

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3


Proposed Method

Fig. 1 shows flowchart of our proposed method. Differential excitation and gradient of an image is computed independently. Dense texture feature for the gradient image is obtained by partitioning the image into small spatial regions and histogram is obtained for these spatial regions. The shape and appearance of local object in an image is represented by local intensity gradients or edge directions. The gradient is calculated over the complete image, which gives complete information about the edges. Then image is divided into small spatial cells, and histograms are calculated over those cells. The overlapping of cells is used to make it more distinctive and powerful for identifying the edges, which provides the edge orientations and gradient directions over the pixels of the cell. After getting the histograms from each cell, image is formed from these histograms. Gradient of image provide the information that how fast image is changing, however it does not define the relationship among the neighboring pixels. So, we use the differential excitation to get the relative intensity difference of a current pixel and its neighbor’s. Finally, the differential excitation and gradient information is concatenated. 2D histogram of differential excitation and gradient orientation is computed as follows:

Fig. 1. Flowchart of proposed method


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1. Compute the differential excitation and gradient of each pixel in cell. 2. Quantize the gradient information into 8 dominant orientations. Map the differential excitation into 256 bins by[22]. 3. Compute the histogram of each gradient orientation by accumulating the differential excitation showing the same gradient orientation. 4. Cut the histograms of each gradient orientation into M=6 segmentation obtained from step 3. 5. Assign the Weight to each segmented area as in [6]. 6. Concatenate eight segmentations from eight dominant orientations into one histogram. We can get 6 histograms. 7. Concatenate these M=6 histograms into one histogram, which is the final histogram.

4

Results and Discussion

The experiments have been conducted on two datsets: Brodatz [4] and KTHTIPS2-a [7]. Brodatz data set contains 2,048 sample images. There are total 32 texture categories with 64 samples in each category. Some examples of Brodatz textures used in our experiments are shown in Fig. 2. First row has the images of size 256x256 pixels having 256 gray levels. The KTH-TIPS2-a database contains 4 physical, planar samples of each of 11 materials under varying illumination, pose and scale. Some examples from each sample are shown in Fig. 2 (second row) .The KTH-TIPS2-a texture dataset contains 11 texture classes with 4,395

(a)

(b)

(c)

(d)

(e)

(f )

Fig. 2. Some examples of images from data set KTH-TIPS2-a (first row) and Brodatz (second row)

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KT H − T IP S2 − a

Brodatz Fig. 3. Some examples of images from data set KTH-TIPS2-a (first row) and Brodatz (second row)

images. The images are 200x200 pixels in size, and they are transformed into 256 gray levels. The database contains images at 9 scales, under four different illumination directions, and three different poses. We have compared our method with state-of-the-art texture classification methods like LBP [4], Local Phase Quantization (LPQ) [5] and WLD [6]. The performance of proposed method has been evaluated in terms of accuracy, Accuracy = correctly classified images/total number of images. Texture classification is a basic problem in computer vision with a wide variety of applications [23]. From Fig. 3, we can observe that for Brodatz dataset proposed method performs well over the state-of-the-art methods. Also for KTHTIPS2-a dataset, where we have got eight, six and four percent better results as compared to LBP [4], LPQ [5], and WLD [23] respectively.


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For the verification of our proposed method, experiments have been conducted ten times with randomly selected training and testing images. The neighbour’s values of LBP and LPQ has been used as in [4]. The correlation coefficient of LPQ set to ρ = 0.9 in the experiments. In WLD, the histogram of an image is obtained after computing the gradient orientation of an image, which leads to lack of information in small spatial regions while computing the histogram of complete image for classification. In proposed method after computing the gradient information of the image, image is divided into small spatial regions. Then the histogram of those small spatial regions is computed, which defines the texture of the image at cell level effectively. At cell level, information of edges is more compact, so it obtains strong local contrast information and reduces the blurring. In WLD, the computation of the gradient of an image did not cater the information of edges effectively, the local contrast information is not sufficient and also blurs the edges of an image, results into decrease the texture recognition rate.

5

Conclusion

In this paper, an improvement of Weber’s Local Descriptor has been proposed. Orientation component of WLD has been computed by using Histogram of gradient of an image. At first, the image is partitioned into small spatial regions and then histogram is calculated. By applying the histogram of gradient on small spatial regions, the compact information of image texture is obtained. SVM classifier is used for the classification. Our proposed method outperforms over the state-of-the-art methods like LBP, LPQ and WLD in term of classification accuracy. Acknowledgments. The research work described in this paper was fully supported by the grants from the National Natural Science Foundation of China (61375045) and Beijing Natural Science Foundation (4142030). Prof. Ping Guo is the author to whom all correspondence should be addressed.

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