Abstract
The present paper discusses the essential tenets of the method whose purpose is to enable effective search of images of given rock in multimedia databases. The search is based exclusively on an image request, to which the system’s response is a set of images presenting visually similar rocks. The images that constitute the basis of the discussed research had been registered with an optical microscope. The collection of images that were used in the process of performing measurements encompassed 5700 digital images presenting 19 rock types. The proposed method is based on the application of image analysis and artificial intelligence concepts. The very process of inference, in turn, makes use of the methods of data classification and grouping. In the paper, the authors demonstrate that these may turn out to be effective mathematical methods, successfully applied to the problem of image search, performed with imagings presenting rock textures. The discussed system concept, based on a feature space defined by the authors, successfully matches up images with the reference standard. The effectiveness rate of that process is very high (very often, it is 100 %). Failed classifications concern only the images which differ visually—in a considerable way—from the rest of the images within a given group. The proposed system concept is to facilitate the decision-making process involved in determining the similarity of investigated objects. In the opinion of the authors, it meets the requirements—and, as such, can be applied to the problem of searching for images in databases, searching discs in order to find images of given rocks and automatic information gain on the basis of video sequences, e.g., in order to find frames presenting particular rock structures.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Aigrain, P., Zhang, H., Petkovic, D.: Content-based representation and retrieval of visual media: a state-of-the-art review. Multimed. Tools Appl. 3(3), 179–202 (1996)
Bach, J.R., Fuller, C.E., Gupta, A., Horowitz, B., Jain, R., Shu, C.F.: U.S. Patent no. 5,893,095, DC: U.S. Patent and Trademark Office, Washington (1999)
Baykan, N.A., Ylmaz, N.: Mineral identification using color spaces and artificial neural networks. Comput. Geosci. 36(1), 91–97 (2010)
Cannon, R.L., Dave, J.V., Bezdek, J.C.: Efficient implementation of the fuzzy c-means clustering algorithms, pattern analysis and machine intelligence. IEEE Trans. 2, 248–255 (1986)
Celeux, G., Soromenho, G.: An entropy criterion for assessing the number of clusters in a mixture model. J. Classif. 13(2), 195–212 (1996)
Dudani, S.A.: The distance-weighted k-nearest-neighbor rule. Systems, man and cybernetics. IEEE Trans (4), 325–327 (1976)
Grunbock, C., King, M.T., Mannby, C.F., Smith, M.J.: U.S. Patent Application 12(964), 662 (2010)
Gudivada, V.N., Raghavan, V.V.: Content based image retrieval systems. Computer 28(9), 18–22 (1995)
Haneberg, W.C.: Simulation of 3D block populations to characterize outcrop sampling bias in bimrocks, Felsbau Rock and Soil Engineering. J. Eng. Geol. Geomech. Tunn. 22(5) (2004)
Hardy, A.: On the number of clusters. Comput. Stat. & Data Anal. 23(1), 83–96 (1996)
Herbin, M., Bonnet, N., Vautrot, P.: Estimation of the number of clusters and influence zones. Pattern Recogn. Lett. 22(14), 1557–1568 (2001)
Holden, E.J., Moss, S., Russell, J.K., Dentith, M.C.: An image analysis method to determine crystal size distributions of olivine in kimberlite. Comput. Geosci. 13(3), 255–268 (2009)
Jain, A.K., Vailaya, A.: Image retrieval using color and shape. Pattern Recogn. 29(8), 1233–1244 (1996)
Kasvand, T.: Computerized vision for the geologist. J. Int. Assoc. Math. Geol. 15(1), 3–23 (1983)
Li, X.R., Zhang, K., Jiang, T.: Minimum entropy clustering and applications to gene expression analysis. In: Computational Systems Bioinformatics Conference, vol. 2004, pp. 142–151 (2004)
Marmo, R., Amodio, S., Tagliaferri, R., Ferreri, V., Longo, G.: Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples. Comput. Geosci. 31(5), 649–659 (2005)
Marques, O., Furht, B.: Content-based visual information retrieval. Distrib. Multimed. Databases: Tech. Appl., 37–57 (2001)
Meng, H.D., Song, Y.C., Song, F.Y., Shen, H.T.: Research and application of cluster and association analysis in geochemical data processing. Comput. Geosci. 15(1), 87–98 (2011)
Mlynarczuk, M., Gorszczyk, A., Slipek, B.: The application of pattern recognition in the automatic classification of microscopic rock images. Comput. Geosci. 60, 126–133 (2013)
Mlynarczuk, M.: Description and classification of rock surfaces by means of laser profilometry and mathematical morphology. Int. J. Rock Mech. Min. Sci. 47(1), 138–149 (2010)
Moore, B.: Principal component analysis in linear systems: controllability, observability, and model reduction, automatic control. IEEE Trans. 26(1), 17–32 (1981)
Obara, B., Kozusnikova, A.: Utilisation of the image analysis method for the detection of the morphological anisotropy of calcite grains in marble. Comput. Geosci. 11(4), 275–281 (2007)
Park, H.S., Jun, C.H.: A simple and fast algorithm for k-medoids clustering. Expert Syst. Appl. 36(2), 3336–3341 (2009)
Porter, M.L., Wildenschild, D.: Image analysis algorithms for estimating porous media multiphase flow variables from computed microtomography data: a validation study. Comput. Geosci. 14(1), 15–30 (2010)
Singh, N., Singh, T.N., Tiwary, A., Sarkar, K.M.: Textural identification of basaltic rock mass using image processing and neural network. Comput. Geosci. 14(2), 301–310 (2010)
Smeulders, A.W., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. Pattern Analysis and Machine Intelligence. IEEE Trans. 22(12), 1349–1380 (2000)
Thompson, S., Fueten, F., Bockus, D.: Mineral identification using artificial neural networks and the rotating polarizer stage. Comput. Geosci. 27(9), 1081–1089 (2001)
Tadeusiewicz, R., Ogiela, M.R.: Medical image understanding technology, artificial intelligence and soft computing for image understanding. Springer-Verlag, Berlin, Heildelberg (2004)
Kurgan, L.A., Cios, K.J., Tadeusiewicz, R., Ogiela, M., Goodenday, L.S.: Knowledge discovery approach to automated cardiac SPECT diagnosis. Artif. Intell. Med. 23(2), 149–169 (2001)
Wagstaff, K., Cardie, C., Rogers, S., Schrdl, S.: Constrained k-means clustering with background knowledge. ICML 1, 577–584 (2001)
Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbour classification. In: Advances in neural information processing systems, pp. 1473–1480 (2005)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
About this article
Cite this article
Ładniak, M., Młynarczuk, M. Search of visually similar microscopic rock images. Comput Geosci 19, 127–136 (2015). https://doi.org/10.1007/s10596-014-9459-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10596-014-9459-2