The Survey Paper on Clothing Pattern and Color Recognition for Visually Impaired People

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 09, 2015 | ISSN (online): 2321-0613

The Survey Paper on Clothing Pattern and Color Recognition for Visually Impaired People Honey Mishra Department of Electronics and Telecommunication & Engineering Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh, india Abstract— For visually handicapped people a mental support is important in their daily life and participation in a society. The select clothes with complex patterns, colors of clothes is a challenging task for visually impaired people.

We have developed a camera-based system that is use to recognizes clothing patterns in four different categories that is (plaid, striped, patternless, and irregular) and identifies different 11 clothing colors. We used the CCNY Clothing Pattern dataset. The system integrates a camera, a microphone, a computer, and a Bluetooth earpiece for audio description of clothing patterns and colors. The output of this system is audio signal. The clothing patterns and colors are described to blind users verbally. This system can be controlled by speech input through microphone. in this survey paper I provide a comparison report among all technique which are provide a clothing pattern and color recognition of cloth for visually impaired people.

Key words: Clothing Pattern, Color Recognition

(a) (b) Fig. 1: Intraclass variations in clothing pattern images and traditional texture images. (a) Clothing pattern samples with large intraclass pattern and color variations. (b) Traditional texture samples with less intraclass pattern and intensity variati.

I. INTRODUCTION

II. OPTIMIZATION METHOD

Based on the 2002 world population and based on the World Health Organization (WHO), there are more than 161 million people are visually impaired in the world [1].

Choosing of cloth is a challenging task for blind people with suitable color and pattern in their daily life. Most blind or visually impaired people manage this task with the help from their family members or through using plastic Braille nlabels or different types of stitching [2].Blind people require A physical support, for example an infrastructure equipment in a public area for those people to safely move, human support like guiding or taking care of them and a mental support is very important. They are always worried about their dresses and how they look, even if they cannot recognize the color and pattern of their cloth. However, there have been few studies about this support. Therefore we had proposed the basic system [3] which can recognize colors and patterns of clothes. Camera-based clothing pattern recognition is a challenging task due to many clothing pattern and color designs as well as corresponding large intraclass variations [4]. Existing texture analysis methods mainly focus on textures with large changes in viewpoint, orientation, and scaling, but with less intraclass pattern and intensity variations (see Fig. 1). We have observed that traditional texture analysis methods [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]can- not achieve the same level of accuracy in the context of clothing pattern recognition.

In an extension to [15], the system can handle clothes with complex patterns and recognize clothing patterns into four different categories (plaid, striped, patternless, and irregular) to meet the basic requirements based on our survey with ten blind participants. the system is able to identify different colors: red, orange, yellow, green, cyan, blue, purple, pink, black, grey, and white

A. Method 1 1) Expression of Color: Color Coordinate System (PCCS) [15] Japan Color Research Institute propos the practical color co-ordinate system [15], color are define with “tone” and “color name”.

The brightness and saturation are described by tone. Tones are integrated so that the blind person can know their features using the system’s voice response. All tones are integrated into seven tones, they are pale (p and lt), dull (sf and d), grayish (ltg and g) and dark (dk and dkg). The eleven colors which are red, green, yellow, blue, brown, purple, orange, pink, white, black and gray [16]. So “color name” is expressed with voice signal.

2) Recognition of Color:

Fig. 2: flow chart of clothing color recognition The specimen image are capture by the camera are mapped to L*a*b* color space, and this image is divided into less than four clusters by the rule of K-means. The L*a*b* are the three axis of color space are quantized in to 25 different

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The Survey Paper on Clothing Pattern and Color Recognition for Visually Impaired People (IJSRD/Vol. 3/Issue 09/2015/187)

levels. The number of pixels of the image is counted in each voxel, and the four top most voxels are selected in terms of its numbers. Among these four selected voxels, the mid position of pixels is calculated and it is set as the initial value. Thereafter in this method all pixels of the image are classified into four clusters [B].

3) Expression of Pattern: The clothing pattern are divided in to five categories : “vertical- stripe,” “horizontal-stripe,” “checker,” “plain,” and “others,” the input image is passes with a low-pass filter to avoid detecting texture patterns; hence, any unevenness on the surface of the cloth is disregarded, and the clothing image is converted in two-dimensional pattern.

4) Recognition of patternThe flow chart of pattern recognition is shown in Fig. 3. An image is converted to grayscale and then smoothed to suppress the influence of texture. The image is filtered by using Fast Fourier Transform in a spatial frequency domain, and a smoothing image is obtained by Inverse Fast Fourier Transform. Then the Sobel operator is applied to the inverse fast fourier image which is smooth image and vertical edges are detected. Standard deviation is calculated for a distribution of horizontal luminance values, rotating the vertical edge of the image by 1◦ counterclockwise. By this process pattern of the cloth are recognized.

principle orientation of an image. The image is then rotated according to this dominant direction to achieve rotation invariance.

Fig. 3: Flow Chart of Pattern Recognition

Fig. 5: The computation of STA on wavelet subbands.

3) Scale Invariant Feature Transform Bag of Words The local image features has a number of applications, such as image retrieval, and recognition of object, texture, and scene categories[18]. This has development of several local image feature detectors and descriptors. Generally, detectors are used to detect interest points by searching local extrema in a scale space; descriptors are employed to compute the representations of interest points based on their associated support regions.

B. Model 2 The captured image of cloth has two feature that is Local feature and Global feature. The local feature of image is only capture area of the target system. and Global features including directionality and statistical properties of clothing patterns are more stable within the same category. In our proposed system we present extractions of global and local features for clothing pattern recognition, i.e., Radon Signature, statistical descriptor (STA), and scale invariant feature transform (SIFT).

1) Radon Signature: Clothing images has large intraclass variations, which provide the major challenge for clothing pattern recognition for the blind people. the clothing patterns of plaid and striped are both anisotropic and patternless and irregular are isotropic. For this use a novel descriptor, i.e., the Radon Signature, to characterize the directionality feature of the patterns of cloth. Radon Signature is based on the Radon transform [17]which is commonly used to detect the

Fig. 4: Computation of RadonSig. 2) Statistics of Wavelate Subbands The discrete wavelet transform (DWT) decomposes the image into low-frequency channel Dj (I) under a coarser scale and multiple high-frequency channels under multiple scales Wk,j (I);k =1 ,2,3;j =1 ,2,...,J, where J is the number of scaling levels. Therefore, in every scaling level j there is four wavelet subbands including one low-frequency channel Dj (I) and three high-frequency channels Wk,j (I). The highfrequency channels Wk,j (I);k =1 ,2,3 encode the discontinuities of an image along horizontal, vertical, and diagonal directions, respectvely.

Fig. 6: Process of local image feature extraction

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The Survey Paper on Clothing Pattern and Color Recognition for Visually Impaired People (IJSRD/Vol. 3/Issue 09/2015/187)

C. Model 3 Detecting the feature is the important method of classifying the patterns. Each image has its own different characteristics for other[19][20]. These features can be extracted using the following algorithms.  Statistical feature extraction (STA)  Scale Invariance feature transform (SIFT)  Recurrence Quantification Analysis (RQA) 1) Statica Feature Extration Statistical feature extraction is done by the use of wavelet transform. The STA are used to decompose the image pixel into low pixels. STA has 4 features this are variance, energy, uniformity and entropy. Using these features the images can be classified.

2) Scale Invariance Feature Transform (SIFT)SIFT is used for the local feature extraction. To perform recognition, it is very important that the global and local features extracted from the image be identified even under changes in image scale, noise and illumination, as the name mentioned it is invariant to the scale. The features extracted are points, patches in the image.

3) Recurrence Quantification Analysis (RQA)Recurrence Quantification Analysis (RQA) is also provide a local feature extractor. Mainly it is used for the increase accuracy in the SVM classifier[21] [22]. RQA has three feature they are  Recurrence Plot – It is a graph that shows all the time at Which a state of the dynamical system recurs.  Recurrence rate- It is the percentage of points in the threshold plot.

III. DISCUSSION From the method 1 the color and pattern recognition is shown in Table I for every tone of PCCS. Cloth color pattern Stripe 70.5 90.4 Checker 69.7 97.2 Plain 95.6 88.3 Irregular 76.3 98.0 regular 86.2 100.0 Total 78.4 94.4 Table 1: Output of different color and pattern From the method 2 the color and pattern recognition is and their contain percentage are show in table II.

Table 2: output for all different pattern and color From the method 3 the color and clothing pattern are explain in table III.

Table 3: output for all different pattern and color

IV. CONCLUSION The above three method are used for clothing pattern and color recognition. This all method is provide help for blind people to recognize the clothing pattern and color of cloth.

The images are taken from CCNY database in order to implement the system. Output of this method are voice signal which is main advance of this method .the output of method 2 and 3 are same w3hich provide a 98% accurate result and method 1 provide 94% of accurate result.

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