Abstract
Content-based image retrieval (CBIR) has drawn much interest from the research community over the past decade, as a good number of CBIR techniques, methods and systems have emerged, contributing new solutions to the issue of storing, managing and retrieving images, as database management systems do with structured data. There is undoubtedly a crucial need to characterize image content as well as subjectivity in the interpretation of this content (for which the community has coined the term “semantic gap”). In this paper the CBIR system developed by our research group, Qatris iManager, is described as a positive proposal to cope with chief issues in the field, especially the semantic gap, from a novel and original perspective. Based on color, texture and shape features, our system provides a broad range of useful operations to facilitate the storage, management, retrieval and browsing of large image collections. Local and remote image loading processes enable the population of image collections. Classification methods allow users to organize the collections according to their own interests. A multidimensional access method contributes to the efficiency in similarity searches. Parameterized similarity functions give flexibility to the search by content processes. Finally, the integrated automatic learning methods for classification and search processes teach the system about the user’s information needs. The proposed system is the result of a joint effort with different research tasks. This paper extensively describes all the system functionalities, techniques, processes and algorithms implemented.













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Notes
For two square matrices of the same size, \({\mathbf {A}} \succeq {\mathbf {B}}\), denotes that \({\mathbf {A-B}}\) is positive semidefinite.
References
Agresti, A.: Introduction to Categorical Data Analysis. Wiley, New York (1996)
Arias-Nicolás, J.P., Calle-Alonso, F.: A novel content-based image retrieval system based on Bayesian logistic regression. In: Proceedings of 18th International Conference in Central Europe on Computer Graphics. Visualization and Computer Vision’2010 (WSCG’2010) POSTER, pp. 19–22. Science Press, PLZEN, Czech Republic, UNION Agency (2010)
Arias-Nicolás, J.P., Martín, J., Pérez, C.: A logistic regression-based pairwise comparison method to aggregate preferences. Group Decis. Negot. 17(3), 237–247 (2008)
Barrena, M.: Técnicas de Particionamiento Multidimensional Basadas en la Utilizacin de Indices Multiatributo en Bases de Datos Paralelas. Universidad Politécnica de Madrid, Tesis Doctoral (1995)
Batko, M., Falchi, F., Lucchese, C., Novak, D., Perego, R., Rabitti, F., Sedmidubsky, J., Zezula, P.: Building a web-scale image similarity search system. Multimed. Tools Appl. 47(3), 599–629 (2010)
Bolettieri, P., Esuli, A., Falchi, F., Lucchese, C., Perego, R., Piccioli, T., Rabitti, F.: CoPhIR: a Test Collection for Content-Based Image Retrieval, CoRR. arXiv:0905.4627 (2009)
Caro, A., Rodríguez, P.G., Antequera, T., Palacios, R.: Feasible application of shape-based classification. In: Proceedings of IbPRIA (2), Lecture Notes in Computer Science, vol. 4478, pp. 588–595. Springer, Berlin, Heidelberg (2007)
Carrión, P., Cernadas, E., Gálvez, J.F., Damián, M., de Sá-Otero, P.: Classification of honeybee pollen using a multiscale texture filtering scheme. Mach. Vis. Appl. 15(4), 186–193 (2004)
Chatterjee, S., Bhattacherjee, A.: Genetic algorithms for feature selection of image analysis-based quality monitoring model: an application to an iron mine. Eng. Appl. Artif. Intel. 24(5), 786–795 (2011)
Chen, S., Donoho, D., Saunders, M.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)
Chu, A., Sehgal, C.M., Greenleaf, J.F.: Use of gray value distribution of run lengths for texture analysis. Pattern Recogn. Lett. 11(6), 415–419 (1990)
Cinque, L., Levialdi, S., Pellicanò, A., Olsen, K.A.: Color-based image retrieval using spatial-chromatic histograms. In: Proceedings of ICMCS ’99, IEEE International Conference on Multimedia Computing and Systems Volume II, vol. 2, p. 969. IEEE Computer Society, Washington DC (1999)
Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches and trends of the new age. In: Proceedings of the MIR ’05, 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 253–262. ACM, New York (2005)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 1–60 (2008)
Durán, M.L., Rodríguez, P.G., Arias-Nicolás, J.P., Martín, J., Disdier, C.: A perceptual similarity method by pairwise comparison in a medical image case. Mach. Vis. Appl. 21(6), 865–877 (2010)
El-Naqa, I., Yongyi, Y., Galatsanos, N., Nishikawa, R., Wernick, M.: A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE T. Med. Imaging 23(2), 1233–1244 (2004)
Gacquer, D., Delcroix, V., Delmotte, F., Piechowiak, S.: Comparative study of supervised classification algorithms for the detection of atmospheric pollution. Eng. Appl. Artif. Intel. 24(6), 1070–1083 (2011)
Galloway, M.: Texture analysis using gray level run lengths. Gr. Model Im. Proc. 4, 172–199 (1975)
Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.: The amsterdam library of object images. Int. J. Comput. Vis. 61, 103–112 (2005)
Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.: The amsterdam library of object images. http://staff.science.uva.nl/~aloi/ (2005). Accessed 08 March 2011
Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision. Addison-Wesley Longman Publishing, Boston (1992)
Henrich, A., Six, H.W., Widmayer, P.: The LSD-tree: Spatial access to multidimensional point and non-point objects. In: Proceedings of the Very Large Databases Conference, pp. 45–53 (1989)
Hjaltason, G., Samet, H.: Ranking in spatial databases. In: Proceedings of Symposium on Large Spatial Databases, pp. 83–95 (1995)
Jain, M., Singh, S.K.: A survey on: content based image retrieval systems using clustering techniques for large data sets. Int. J. Manag. Inf. Technol. 3(4), 23–39 (2011)
Jin, X., French, J.C.: Improving image retrieval effectiveness via multiple queries. In: Proceedings of ACM International Workshop on Multimedia Databases, pp. 86–93 (2003)
Jurado, E., Barrena, M.: Effcient similarity search in feature spaces with the q-tree. In: Proceedings of Advances in Databases and Information Systems, pp. 177–190 (2002)
Krishnapuram, B., Carin, L., Figueiredo, M., Hartemink, A.: Sparse multinomial logistic regression: fast algorithms and generalization bound. IEEE T. Pattern Anal. 27(6), 957–968 (2005)
Khan, F., Anwer, R., van de Weijer, J., Bagdanov, A., Vanrell, M., Lopez, A.: Color attributes for object detection. In: Proceedings of CVPR, pp. 3306–3313 (2012)
Lee, C.F., Chang, W.T.: Recovery of color images by composed associative mining and edge detection. Inf. Hiding Multimed. Sign. Proc. 1, 310–324 (2010)
Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE T. Pattern Anal. 25(9), 1075–1088 (2003)
Li, J., Wang, J.Z.: Alip: the automatic linguistic indexing of pictures system. CVPR 2, 1208–1209 (2005)
Li, J., Qian, X., Tang, Y., Yang, L., Liu, C.: GPS estimation from users’ photos. Proc. MMM 2013, 118–129 (2013)
Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40, 262–282 (2007)
Lomet, D., Salzberg, B.: The hB-tree: a multiattribute indexing method with good guaranteed performance. ACM T. Database Syst. 14, 625–658 (1990)
MacDonald, L., Luo, M.: Colour Image Science: Exploiting Digital Media. Wiley, Chichester (2002)
Mäenpää, T., Pietikäinen, M.: Multi-scale binary patterns for texture analysis. Lect. Notes Comput. Sci. 2749, 885–892 (2003)
Müller, H., Müller, W., Marchand-Maillet, S., Pun, T., Squire, D.: A framework for benchmarking in CBIR. Multimed. Tools Appl. 21, 55–73 (2003)
Nie, L., Yan, S., Wang, M., Hong, R., Chua, T.: Harvesting visual concepts for image search with complex queries. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 59–68 (2012)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of CVPR, pp. 2161–2168 (2006)
Ojala, T., Menp, T., Pietikinen, M., Viertola, J., Kyllnen, J., Huovinen, S.: Outex: new framework for empirical evaluation of texture analysis algorithms. In: Proceedings of International Conference on Pattern Recognition, pp. 701–706 (2002)
Palenichka, R., Lakhssassi, A., Zaremba, M.: Adaptive multi-scale segmentation of surface data using unsupervised learning of seed positions. Eng. Appl. Artif. Intel. 24, 822–832 (2011)
Qian, X., Hua, X., Chen, P., Ke, L.: An effective local binary patterns texture descriptor with pyramid representation. Pattern Recogn. 44, 2502–2515 (2011)
Quirós, E., Felicísimo, A.M., Cuartero, A.: Testing multivariate adaptive regression splines (MARS) as a method of land cover classification of TERRA-ASTER satellite images. SENSORS 9(11), 9011–9028 (2009)
Reyes, C., Durán, M.L., Alonso, T., Rodríguez, P.G., Caro, A.: Behaviour of texture features in a CBIR system. Lecture Notes Artif. Intell. Hybrid Artif. Intell. Syst. 5271, 425–432 (2008)
Robinson, J.T.: The K-D-B-tree: a search structure for large multidimensional dynamic indexes. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 10–18 (1981)
Rodríguez, F., Barrena, M.: A fast and robust bulk-loading algorithm for indexing very large digital elevation datasets I. Algorithm. Comput. Geosci. 37, 804–813 (2011)
Rodríguez, F., Barrena, M.: A fast and robust bulk-loading algorithm for indexing very large digital elevation datasets II. Exp Res. Comput. Geosci. 37, 814–821 (2011)
Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: Proceedings of SIGMOD ’95, 1995 ACM SIGMOD International Conference on Management of data, pp. 71–79 (1995)
Rui, Y., Huang, T.S., Chang, S.F.: Image retrieval: current techniques, promising directions and open issues. J. Vis. Commun. Image R. 10(1), 39–62 (1999)
Ryan, T.P.: Modern Regression Methods. Wiley, New York (1997)
Saber, E., Tekalp, A.M., Eschbach, R., Knox, K.: Automatic image annotation using adaptive color classification. Gr. Model Im. Proc. 58(2), 115–126 (1996)
Sadat, R.M.N., Mottalib, A., Hasan, S.F., Salehin, M.N.: Multimodal image classification using inverted local patterns. In: Proceedings of the IEEE International Conference on Multimedia and Expo, pp. 1–6 (2011)
J. Sivic, J., Zisserman, A.: Video Google: A text retrieval approach to object matching in videos. In: Proceedings of International Conference on Computer Vision, vol. 2, pp. 1470–1477 (2003)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE T. Pattern Anal. 22(12), 1349–1380 (2000)
Vailaya, A., Zhang, H., Yang, C., Liu, F., Jain, A.K.: Automatic image orientation detection. IEEE T. Image Process. 11(7), 746–755 (2002)
Vasconcelos, N.: From pixels to semantic spaces: advances in content-based image retrieval. IEEE Comput. 40(7), 20–26 (2008)
Veltkamps, R.C., Tanase, M.: Content-based image retrieval systems: a survey. Technical Report TR UU-CS-2000-34 (revised version), Department of Computing Science, Utrecht University (2002)
Venters, C.C., Cooper, M.D.: A review of content-based image retrieval systems. Technical Report, Manchester Visualization Centre, Manchester Computing, University of Manchester, Manchester, UK (2000)
Wang, C., Zhang, L., Zhang, H.-J.: Learning to reduce the semantic gap in web image retrieval and annotation, In: SIGIR ’08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, vol. 24, pp. 355–362 (2008)
Zhang, H., Petkovic, D.: Content-based representation and retrieval of visual media: a stat-of-art revieew. Multimed. Tools Appl. 3, 179–202 (1996)
Zhang, H., Su, Z.: Relevance feedback in CBIR. In: Proceedings of the IFIP TC2/WG2.6 Sixth Working Conference on Visual Database Systems, pp. 21–35 (2002)
Zhang, S., Huang, Q., Hua, G., Jiang, S., Gao, W., Tian, Q.: Building contextual visual vocabulary for large-scale image applications, In: Proceedings of the International Conference on Multimedia, pp. 501–510 (2010)
Zheng, L., Wang, S., Liu, Z., Tian, Q.: Packing and Padding: Coupled Multi-index for Accurate Image Retrieval, CoRR. arXiv:1402.2681 (2014)
Zhou, X.S., Huang, T.S.: CBIR: From low-level features to high-level semantics. In: Proceedings of SPIE Image and Video Communication and Processing, pp. 24–28 (2000)
Zhou, W., Tian, Q., Lu, Y., Yang, L., Li, H. Latent visual context learning for web image applications. Pattern Recogn. 44(10), 2263–2273 (2011)
Acknowledgments
This research was partially supported by the Spanish Government (Ministerio de Ciencia e Innovación and Junta de Extremadura) and the European Union (FEDER) via projects TIN2005-05939 TSI2007-66706-C04-03, TIN2008-06796-C04-03, PDT09A009 and TIN2008-03063).
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Barrena, M., Caro, A., Durán, M.L. et al. Qatris iManager: a general purpose CBIR system. Machine Vision and Applications 26, 423–442 (2015). https://doi.org/10.1007/s00138-015-0672-3
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DOI: https://doi.org/10.1007/s00138-015-0672-3
Keywords
- Content-based image retrieval (CBIR)
- Semantic gap
- Feature extraction
- Indexing
- Image classification
- Relevance feedback
- Automatic learning