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

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

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.Fig 2. Shape description for bracelet

( 3 )Whereis 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, Kurtosisare approximated as ( 7 ) follows as:

and points with the magnitudeis the count of points that can be calculated byandway 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 )( 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 Discussion

The 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

COARSECape CONSTRAST Directivity Roughness Regularity
1.989226 89.


85.1901 0.64729 1.733
1.989615 82.4277 83.


0.92347 1.218
1.989271 77.6203 78.9798 0.


1.890989 86.9364 78.5963 0.


1.986763 67.6203 76.7307 0.56433 1.698
1.896537 89.


65.8764 0.23453 0.734
1.795653 79.


61.8797 0.34587 1.345
1.896764 84.


67.5643 0.64549 1.456


97.7686 70.6565 0.56547 1.563


86.9364 75.9915 0.45637 1.324
1.894555 16.5529 97.


0.08778 3.987
1.786455 22.1887 81.8739 0.09878 2.


1.983675 27.7659 89.9737 0.14256 4.874
1.654764 19.1272 90.


0.28782 2.765
1.546478 12.0102 87.


0.36387 3.785

Fig 4. Classified Bangle OutputFig 4 shows the images of trial images of each assortment.

It is seen that every sample is classified to allow categories.TABLE V. Confusion matrix for the proposed method

Overall Accuracy ( 86.66 % ) Target Classs
Plain bracelet Kada bracelet Stone Bangle
Plain bracelet 85 % 10 % 5 %
Kada bracelet 4 % 89 % 7 %
Stone bracelet 0 % 14 % 86 %

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

Type of classifier Accuracy
SVM 86.66 %
KNN 82 %

With a proper preparation procedure, it shows that SVM outperforms KNN classifier by 5 % .

IV. Decision

This 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.


  1. Kumar D, Vinu Prasad G, ” Image Processing Techniques to Acknowledge and Classify Bottle Articles” , International Journal of Computer Applications. Volume 2, 2012, 101-104.
  2. S. F.

    Lilhare ; DrN. G. Bawane, ” Classification of paddy assortments utilizing image processing” , International Journal of Computer Applications, 2012, Volume 1, 32-45.

  3. R.M. Haralick, K. Shanmugam, and I.

    Einstein, “Textural Features for Image Classification” , IEEE Transactions on Systems, Man and Cybernetics, 1973. 610-621.

  4. Tamura Hideyuki, Shunji Mori, and Takashi yamawaki, “Textural Features Matching to Visual Perception” , IEEE Transaction of systems Man and Cybernetics.1978, 460-473.

  5. V. Vapnik. The Nature of Statistical Learning Theory. New york: Springer-Verlag. 1995.
  6. Lei Zhang, Tsinghua Univ.

    , ” support vector machine for image retrieval” , IEEE Transaction on Image processing. 721 – 724 Vol. 2, 2001.

  7. Bottema, ” Circularity of objects in images” , IEEE International Conference on continuing Acousticss, Speech, and Signal Processing, Volume:6.

    2000, pg. 2247 – 2250 Vol. 4.

  8. M. J. Monika Sahu, M.A. Rizvi, ” optimized Image Retrieval System: Texture and Shape Based Approach” , International Journal of Computer Application, Volume 4, 2013, 201-213.
  9. Sklansky, J, “Image Segmentation and Feature Extraction, ” IEEE Transactions on Systems, Man, and Cybernetics, SMC-8, pp.

    237-247, 1978.

  10. R. C. Gonzalez, R. E. Woods, “Digital Image Processing” .

    Prentice Hall, 2004

  11. Juan Martinez-Alajarin, Jose D. Luis-Delgado, and Luis M. Tomas Balibrea.

    ”Automatic System for Quality-Based Classification of Marble Textures” , IEEE minutess on systems, adult male, and cybernetics—part degree Celsius: applications and reappraisals, vol. 35, no. 4, November 2005.

  12. Somkait Udomhunsakul, Pichet Wongsita, ” Feature Extraction in Medical MRI Images ” Proceedings of IEEE Conference on Cybernetics and Intelligent Systems Singapore, 1-3 December, 2004.
  13. Durgesh K.

    , Srivastava, and Lekha bhambhu, ” Data Classification utilizing Support Vector Machine” , Journal of Theoretical and Applied Information Technology, volume 23,2005 – 2009.

  14. H.C Lin, L.L. Wang, and S.N Yang, ” Extracting cyclicity of a regular texture based on car right map, ” Pattern Recognition Letters, Vol 18, no.

    5, pp. 433-443,1997.

  15. Ya-Li Qi, ” A Relevance Feedback Retrieval Method Based on Tamura Texture” , IEEE, published on Knowledge Acquisition and Modeling, Nov. 30 2009-Dec. 1 2009.
  16. Chen Xiao-ying, Ai-guo Song, Yuan Wen, Zhen Jun-jie, ” On the application of BEMD and Tamura textural characteristic for acknowledging ground-based cloud” , International Conference on Computer Application and System Modeling,22-24 Oct. 2010.
  17. Boba Wang, Hong Bao, Shan Yang, Haitao Lou.”Crowd Density Estimation Based on Texture Feature Extraction” , Journal of Multimedia, Vol, no 4, pg no.331-337, Aug 2013.

I'm Ruth!

Would you like to get a custom essay? How about receiving a customized one?

Check it out