Classification of Gold Bangles Based on Tamura Texture Features Essay
Categorization of Gold Bangles based on Tamura Texture FeaturesAbstract— Mortgaging gold for money in the bank is common in India. Banks rely on assessor’s accomplishments to prove the pureness of gold, its weight and supply a description of the points. The absence of skilled assessor makes the loan allowing procedure boring and clip devouring when the quantum of gilded points is mortgaged. This paper provides an image processing solution to automatically supply a description of the mortgage of gold bracelet that would go a ready to hand note for borrowers every bit good. This work classifies the gold bracelets by disk shape and texture characteristics. The proposed work is oriented towards sorting the bracelet into different categories as field bracelet, Stone bracelet and kada bracelet utilizing SVM classifier. Its truth is obtained as 86.
66 % and KNN classifier is used for comparing.Index Footings —facet ratio, disk shape, texture characteristics, KNN classifier, SVM classifier.I
I. Introduction
N the present Indian scenario, gold has become an of import portion of life. The jewellery industry maintains an of import place for economic growing in Indian economic system.
It is a taking foreign exchange earner, every bit good as one of the fastest turning sectors in the state. Among the different types of cherished metals used, gold is one of the most expensive and tempting metal. There are many types of jewelleries which vary in a immense scope of forms and designs. Gold jewellery signifiers around 80 % of the Indian jewellery market, with the balance comprising studded jewellery that includes diamond and other cherished and semi-precious rocks.
Among the different types of jewelleries, gold bracelets are the widely used 1s by adult females today.Object acknowledgment and categorization has been an country of involvement in the field of Image Processing [ 1 ] . Though many research workers have put attempts on acknowledgment and categorization of solid articles, this work attempts to sort the bracelet into a Plain bracelet, Kada bracelet and Stone bracelet. In any object sensing attack, input images may incorporate objects of changing size and textures [ 2 ] . Hence, placing bracelets among other decorations and sorting them into several categories is a ambitious undertaking.Haralick et Al.
[ 3 ] have developed a set of characteristics for sorting or categorising pictural informations based on grey tone spacial dependences, and illustrated their application in category designation undertakings. Tamura et al [ 4 ] have proposed textural characteristics which correspond to human ocular perceptual experience. Six basic textural belongingss ( i.
e. , saltiness, contrast, directivity, line-likeness, regularity, and raggedness ) have been measured by human topics. Ya-Li Qi [ 16 ] proposed a relevant feedback retrieval method based on texture characteristics. In their work, Tamura characteristic is used as a texture characteristic to work out the semantic spread. Ground-based cloud acknowledgment for automatic cloud observation utilizing Tamura characteristic is proposed by Chen Xiao-ying [ 17 ] . Bobo wang et Al [ 18 ] used the Tamura characteristic for gauging the crowd denseness.
The remainder of this paper is organized as follows: Section II explains about the proposed methodological analysis. The Experiments and consequences are reported in Section III and concluding subdivision IV concludes the proposed method.
II. Proposed methodological analysis
The proposed method helps to place bracelets among different gold decorations.
A box covered with black coloring sheet is taken into consideration and the decoration which is to be classified is kept indoors. A camera is placed at an angle of 45° and it is kept still. Images are captured with unvarying background and formatted to jpeg for standardisation. The side position of the bracelet is acquired at changeless light and fixed distance. Geometric characteristics and disk shape are measured to place whether the given decoration is a bracelet or non. Aspect ratio is considered for sorting different assortment of bracelets.
The overall Block diagram of the proposed work is shown in Fig 1.
A. Image preprocessing
The primary measure is to change over the RGB colour image to binary image for the given ornament image. Subtract the dilated image from the original image, to obtain the boundary of the decoration. From the outermost boundary, perimeter is calculated. This boundary is localized and further processing is done on it. . The value is 1 for perfect circle and between 1 and 1.5 for oval. A bangle image is acquired in side position to acquire the inside informations about the etched designs.</p>
<p> The form of the acquired bracelet image is ellipse as shown in Fig 2. , whereas for earring, the form differs and therefore the disk shape will be greater than 1.5 as shown in Fig 3.<em>iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ iˆ</em><img alt=)
Fig 2. Shape description for bracelet
( 3 )Where
is obtained as the value which maximizes the differences of the traveling norms along the horizontal and perpendicular waies.
The saltiness can be computed as ( 4 )
( 4 )Where
. A harsh texture will incorporate less figure of big primitives, while a smooth texture contains a big figure of little primitives.
Contrast refers the difference in strength between neighbouring pels and grey degree distribution. A high contrast, texture will hold big differences in strength between neighbouring pels, where a texture of low contrast has little difference. For ciphering the contrast, Kurtosis .</img><img alt=)
are approximated as ( 7 ) follows as:
 | + |?<em><sub>Y</sub></em>(<em>ten, Y</em>) | )</img><img alt=) and points with the magnitude :</img></img></img><img alt=) is the count of points that can be calculated by and way of happening of grey degree strength.The line similitude characteristicFLinis defined as an mean happenstance of the border waies that co-occurred in the braces of pels separated by a distancevitamin Dalong the border way in every pel. The border strength is expected to be greater than a given threshold extinguishing fiddling “ weak ” borders. The happenstance is measured by the cosine of the difference between the angles, so that the accompaniments in the same way are weighted as +1 and those in the perpendicular waies by -1 can be computed as follows ( 10 )</img>Where<img alt=) ( 13 )Where eleven ?? is a instance of the preparation set ( one = 1, … , N ) .
Bing the dimension of the input infinite, and YI? { -1, +1 } is the corresponding category. Among the dividing hyper planes, theSVM attack selects the 1 for which the distance to the closest instance is maximum. Since such a distance is 1/ tungsten, happening the optimum hyper plane is tantamount to minimising tungsten under restraints. III. Results And DiscussionThe scopes of the characteristic extracted are shown in Table IV. It is found that for kada bracelet, the form of the design will be repeated and therefore there will be difference in grey strength. This fluctuation in the grey strength is measured through the Tamura texture characteristic. From the extracted characteristics, it shows that saltiness is the step of strength fluctuation due to repeated fluctuation in primitives, the value will be high for kada bracelet. While for rock bracelet, the saltiness will be similar to kada bracelet but contrast value will be more than a field bracelet. Directionality and roughness value will be relatively higher for kada bracelet than other bracelets. For apparent bracelet at unvarying strength, the regularity texture characteristic will be regular. SVM classifier is used to sort the bracelet into different assortment. Accuracy of SVM is achieved about 86.66 % . Experiment is performed in MATLAB.TABLE IV. Texture Feature Extraction
It is seen that every sample is classified to allow categories.TABLE V. Confusion matrix for the proposed method
The confusion matrix for the proposed method is shown in the Table V. The most evident behaviour of the classifier is quantified by confusion matrix which shows a strong correlativity between true category and categorization consequence. This method achieves the overall truth of 86.66 % .TABLE VI. Accuracy for different classifier
With a proper preparation procedure, it shows that SVM outperforms KNN classifier by 5 % . IV. DecisionThis work has proposed a method to sort gold bracelets based on its texture characteristic. Shape description utilizing geometric characteristics are besides taken into consideration for know aparting bracelet from other decorations. The proposed algorithm uses the efficient characteristics like object disk shape and texture analysis. Based on these characteristics, bracelets are classified into field, Kada and Stone bracelet utilizing SVM. This mechanization is applied in the field of banking for gold ornament mortgage and jewellery store for depicting the cosmetic assortment. V. REFERENCES
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Fig 4. Classified Bangle OutputFig 4 shows the images of trial images of each assortment.