Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features

  • Arvita Agus Kurniasari Politeknik Elektronika Negeri Surabaya
  • Ali Ridho Barakbah Politeknik Elektronika Negeri Surabaya
  • Achmad Basuki Politeknik Elektronika Negeri Surabaya
Keywords: Image Feature Extraction, Content-dependent Image Search, Image Search System, Feature Aggregation

Abstract

The existing image search system often faces difficulty to find a appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by that the users’ intention for searching image is different with dominant information of the image collected from feature extraction. In this paper we present a new approach for content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something - supposed as an object. We applies segmentation of image as content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract color feature of image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)’s approach with 15 kernels.  For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.

References

A. K. Jain and A. Vailaya, Image retrieval using color and shape, Pattern Recognit., vol. 29, no. 8, pp. 1233–1244, 1996.

M. Bounthanh, K. Hamamoto, and B. Attachoo, Content-Based Image Retrieval System Based on Combined and Weighted Multi-Features, Int. Symp. Commun. Inf. Technol., pp. 449–453, 2013.

T. Kato, Database architecture for content-based image retrieval, Proc. SPIE Conf. Image Storage Retr. Syst., vol. 1662, no. March, pp. 112–123, 1992.

C. W. Niblack, QBIC project: querying images by content, using color, texture, and shape, Proc. SPIE, vol. 1908, no. 1, pp. 173–187, 1993.

a. Pentland, R. W. Picard, and S. Sclaroff, Photobook: Content-based manipulation of image databases, M.I.T. Media Laboratory Percaptual Computing Technical Report, vol. 255, no. 3. pp. 233–254, 1993.

G. W. Jia Li James Ze Wang, SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries, IEEE Pami, vol. 23, no. 9, pp. 947–963, 2001.

M. Li and J. Xiuhua, An Improved Algorithm Based on Color Feature Extraction for Image Retrieval. 2016.

A. R. Barakbah and Y. Kiyoki, 3D-Color Vector Quantization for Image Retrieval Systems, Int. Database Forum 2008, no. September 2008, pp. 13–18, 2008.

A. R. Barakbah and Y. Kiyoki, Image Search System with Automatic Weighting Mechanism for Selecting Features, 6th Int. Conf. Inf. Commun. Technol. Syst., 2010.

P. Anantharatnasamy, K. Sriskandaraja, V. Nandakumar, and S. Deegalla, Fusion of colour, shape and texture features for content based image retrieval, Proc. 8th Int. Conf. Comput. Sci. Educ. ICCSE 2013, no. Iccse, pp. 422–427, 2013.

A. K. Naveena and N. K. Narayanan, Image retrieval using combination of color, texture and shape descriptor, 2016 Int. Conf. Next Gener. Intell. Syst. ICNGIS 2016, 2017.

K. Mala and S. Suganya, Content Based Image Retrieval System based on Semantic Information Using Color, Texture and Shape Features, Comput. Technol. Intell. Data Eng., 2016.

A. Kutics and A. Nakagawa, Detecting prominent objects for image retrieval, Proc. - Int. Conf. Image Process. ICIP, vol. 3, pp. 445–448, 2005.

A. Mohamed, F. Khelifi, J. Jiang, and S. Ipson, Efficient content based image retrieval based on Semantic Object Detection, 10th Int. Conf. Inf. Sci. Signal Process. their Appl. ISSPA 2010, no. Isspa, pp. 193–196, 2010.

N. Suciati, D. Herumurti, and A. Y. Wijaya, Fractal-based texture and HSV color features for fabric image retrieval, Proc. - 5th IEEE Int. Conf. Control Syst. Comput. Eng. ICCSCE 2015, no. November, pp. 178–182, 2016.

R. Garnavi, M. Aldeen, M. E. Celebi, A. Bhuiyan, C. Dolianitis, and G. Varigos, Automatic Segmentation of Dermoscopy Images Using Histogram Thresholding on Optimal Color Channels, vol. 1. 2010.

and Y. P. Baogang Wei, Yonghuai Liu, Member, IEEE, Using Hybrid Knowledge Engineering and Image Processing in Color Virtual Restoration of Ancient Murals, IEEE Trans. Knowl. Data Eng., vol. 15, no. 5, pp. 1338–1343, 2003.

T. Adah, H. Ni, and B. Wang, Partial Likelihood for Estimation of Multi-Class Posterior Probabilities, Neural Networks, vol. 21250, pp. 1053–1056, 1999.

A. R. Barakbah and K. Arai, Identifying moving variance to make automatic clustering for normal data set, IECI Japan Work. 2004 2004, no. May, pp. 26–30, 2004.

A. R. Barakbah and K. Arai, Determining Constraints of Moving Variance to Find Global Optimum and Make Automatic Clustering, Ind. Electron. Semin. 2004, pp. 409–413, 2004.

K. Arai and A. R. Barakbah, Hierarchical K-means: an algorithm for centroids initialization for K-means, Rep. Fac. Sci. Engrg. Reports Fac. Sci. Eng. Saga Univ. Saga Univ., vol. 36, no. 1, pp. 36–125, 2007.

A. R. Barakbah and Y. Kiyoki, Image Retrieval Systems with 3D-Color Vector Quantization and Cluster based Shape and Structure Features, Inf. Model. Knowl. Bases XXI, vol. 206, pp. 169–187, 2010.

A. Rosenfeld and J. Pfaltz, Pfalz, J.L, Sequential Operations in Digital Picture Processing. Journal of the ACM 13(4), 471-494, vol. 13. 1966.

Cai-Xia Deng, Gui-Bin Wang, and Xin-Rui Yang, Image edge detection algorithm based on improved Canny operator, 2013 Int. Conf. Wavelet Anal. Pattern Recognit., no. 1, pp. 168–172, 2013.

T. Leung and J. Malik, Representing and recognizing the visual appearance of materials using three-dimensional textons, Int. J. Comput. Vis., vol. 43, no. 1, pp. 29–44, 2001.

J. Z. Wang, Semantics-Sensitive Integrated Matching for Picture Libraries and Biomedical Image Databases, 2000.

Published
2019-06-15
Section
Articles