Content-based image retrieval using a signature graph and a self-organizing map

Van, T. T. ; Le, T. M.

International Journal of Applied Mathematics and Computer Science, Tome 26 (2016) no. 2, pp. 423-438.

Résumé

In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (content-based image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.

Détail

Keywords: binary signature, similarity measure, signature graph, image retrieval
Mots-clés : sygnatura binarna, miara podobieństwa, wyszukiwanie obrazu

Bibliographie

[1] Abdesselam, A., Wang, H.H. and Kulathuramaiyer, N. (2010). Spiral bit-string representation of color for image retrieval, International Arab Journal of Information Technology 7(3): 223–230.

[2] Acharya, T. and Ray, A.K. (2005). Image Processing: Principles and Applications, John Wiley and Sons, Hoboken, NJ.

[3] Alzu’bi, A., Amira, A. and Ramzan, N. (2015). Semantic content-based image retrieval: A comprehensive study, Journal of Visual Communication and Image Representation 32: 20–54.

[4] Bahri, A. and Hamid, Z. (2011). EMD similarity measure and metric access method using EMD lower bound, International Journal of Computer Science Emerging Technology 2(6): 323–332.

[5] Bartolini, I., Ciaccia, P. and Patella, M. (2010). Query processing issues in region-based image databases, Knowledge and Information Systems 25(2): 389–420.

[6] Chappell, T. and Geva, S. (2013). Efficient top-k retrieval with signatures, Proceedings of the 18th Australasian Document Computing Symposium, ADCS’13, Brisbane, Australia, pp. 10–17.

[7] Wang (2016). http://wang.ist.psu.edu.

[8] COREL (2016). http://www.corel.com.

[9] Microsoft (2016). http://research.microsoft.com.

[10] Kim, S., Park, S. and Kim, M. (2003). Central object extraction for object-based image retrieval, Image and Video Retrieval, CIVR 2003, Urbana-Champaign, IL, USA, pp. 39–49.

[11] Kompatsiaris, I. and Strintzis, M.G. (2000). Spatiotemporal segmentation and tracking of objects for visualization of video conference image sequences, IEEE Transactions on Circuits and Systems for Video Technology 10(8): 1388–1402.

[12] Kumar, H.C.S., Raja, K.B., Venugopal, K.R., and Patnaik, L.M. (2009). Automatic image segmentation using wavelets, International Journal of Computer Science and Network Security 9(2): 305–313.

[13] Le, T.M. and Van, T.T. (2013). Image retrieval system based on EMD similarity measure and S-tree, Intelligent Technologies and Engineering Systems, ICITES-2012, Changhua, Taiwan, pp. 139–146.

[14] Li, Y., Jin, J.S. and Zhou, X. (2005). Video matching using binary signature, Proceedings of the IEEE International Symposium on Intelligent Signal Processing and Communication Systems, Hong-Kong, China, pp. 317–320.

[15] Liu, G.-H. and Yang, J.-Y. (2013). Content-based image retrieval using color difference histogram, Pattern Recognition 46(1): 347–357.

[16] Liu, L., Lu, Y. and Suen, C.Y. (2015). Variable-length signature for near-duplicate image matching, IEEE Transactions on Image Processing 24(4): 1282–1296.

[17] Liu, Y., Zhang, D., Lu, G. and Ma, W.-Y. (2007). A survey of content-based image retrieval with high-level semantics, Pattern Recognition 40(1): 262–282.

[18] Manolopoulos, Y., Nanopoulos, A. and Tousidou, E. (2003). Advanced Signature Indexing for Multimedia and Web Applications, Springer Science Business Media, New York, NY.

[19] Marques, O. and Furht, B. (2002). Content-Based Image and Video Retrieval, Springer Science + Business Media, New York, NY/London.

[20] Mezaris, V., Kompatsiaris, I. and Strintzis, M.G. (2004). Still image segmentation tools for object-based multimedia applications, International Journal of Pattern Recognition and Artificial Intelligence 18(4): 701–725.

[21] Muneesawang, P., Zhang, N. and Guan, L. (2014). Multimedia Database Retrieval: Technology and Applications, Springer, Cham/Heidelberg.

[22] Nascimento, M.A. and Chitkara, V. (2002). Color-based image retrieval using binary signatures, SAC 2002, Madrid, Spain, pp. 687–692.

[23] Nascimento, M.A., Tousidou, E., Chitkara, V. and Manolopoulos, Y. (2002). Image indexing and retrieval using signature trees, Data Knowledge Engineering 43(1): 57–77.

[24] Ozkan, S., Esen, E. and Akar, G.B. (2014). Visual group binary signature for video copy detection, Proceedings of the IEEE International Conference on Pattern Recognition, ICPR-2014, Stockholm, Sweden, pp. 3945–3950.

[25] Singha, M. and Hemachandran, K. (2012). Content based image retrieval using color and textual, Signal Image Processing: An International Journal 3(1): 39–57.

[26] Tang, Z., Zhang, X., Dai, X., Yang, J. and Wu, T. (2013). Robust image hash function using local color features, AEU—International Journal of Electronics and Communications 67(8): 717–722.

[27] Van, T.T. and Le, T.M. (2014a). Color image retrieval using fuzzy measure hamming and S-tree, Advances in Computer Science and its Applications, CSA-2013, Vietnam, pp. 615–620.

[28] Van, T.T. and Le, T.M. (2014b). Image retrieval based on binary signature and S-kGraph, Annales Universitatis Scientiarum Budapestinensis de Rolando Eötvös Nominatae, Sectio Computatorica 43(2): 105–122.

[29] Van, T.T. and Le, T.M. (2014c). RBIR based on signature graph, Proceedings of the IEEE International Conference on Computer Communication and Informatics, ICCCI-2014, Coimbatore, India, pp. 1–4.

[30] Wang, X.-Y., Wu, J.F. and Yang, H.Y. (2010). Robust image retrieval based on color histogram of local feature regions, Multimedia Tools and Applications 49(2): 323–345.

[31] Wang, X.-Y., Yang, H.-Y., Li, Y.-W. and Yang, F.-Y. (2013). Robust color image retrieval using visual interest point feature of significant bit-planes, Digital Signal Processing 23(4): 1136–1153.

[32] Yoo, H.-W., Jung, S.-H., Jang, D.-S. and Na, Y.-K. (2002). Extraction of major object features using VQ clustering for content-based image retrieval, Pattern Recognition 35(5): 1115–1126.

[33] Zhou, W., Li, H., and Tian, Q. (2014). Academic Press Library in Signal Processing, Vol. 5, Elsevier, Oxford.