Abstract
This paper describes a method for searching for common sets of descriptors between collections of images. The presented method operates on local interest keypoints, which are generated using the SURF algorithm. The use of a dictionary of descriptors allowed achieving good performance of the content-based image retrieval. The method can be used to initially determine a set of similar pairs of keypoints between images. For this purpose, we use a certain level of tolerance between values of descriptors, as values of feature descriptors are almost never equal but similar between different images. After that, the method compares the structure of rotation and location of interest points in one image with the point structure in other images. Thus, we were able to find similar areas in images and determine the level of similarity between them, even when images contain different scenes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Akhtar, Z., Rattani, A., Foresti, G.L.: Temporal analysis of adaptive face recognition. Journal of Artificial Intelligence and Soft Computing Research 4(4), 243–255 (2014)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Bruzdzinski, T., Krzyzak, A., Fevens, T., Jelen, Ł.: Web-based framework for breast cancer classification. Journal of Artificial Intelligence and Soft Computing Research 4(2), 149–162 (2014)
Chu, J.L., Krzyżak, A.: The recognition of partially occluded objects with support vector machines and convolutional neural networks and deep belief networks. Journal of Artificial Intelligence and Soft Computing Research 4(1), 5–19 (2014)
Drozda, P., Sopyła, K., Górecki, P.: Online crowdsource system supporting ground truth datasets creation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS (LNAI), vol. 7894, pp. 532–539. Springer, Heidelberg (2013)
Edelkamp, S., Schroedl, S.: Heuristic Search: Theory and Applications. Elsevier Science (2011)
Karimi, B., Krzyzak, A.: A novel approach for automatic detection and classification of suspicious lesions in breast ultrasound images. Journal of Artificial Intelligence and Soft Computing Research 3(4), 265–276 (2013)
Makinana, S., Malumedzha, T., Nelwamondo, F.V.: Quality parameter assessment on iris images. Journal of Artificial Intelligence and Soft Computing Research 4(1), 21–30 (2014)
Najgebauer, P., Nowak, T., Romanowski, J., Gabryel, M., Korytkowski, M., Scherer, R.: Content-based image retrieval by dictionary of local feature descriptors. In: 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, July 6-11, pp. 512–517 (2014)
Pena, M.: A Comparative Study of Three Image Matching Algorithms: Sift, Surf, and Fast. BiblioBazaar (2012)
Wang, L., Ju, H.: A robust blob detection and delineation method. In: International Workshop on Education Technology and Training and 2008 International Workshop on Geoscience and Remote Sensing, ETT and GRS 2008, vol. 1, pp. 827–830 (December 2008)
Wang, X., Japkowicz, N., Matwin, S.: Automated approach to classification of mine-like objects using multiple-aspect sonar images. Journal of Artificial Intelligence and Soft Computing Research 4(2), 133–148 (2014)
Zalasiński, M., Cpałka, K.: New approach for the on-line signature verification based on method of horizontal partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS (LNAI), vol. 7895, pp. 342–350. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Najgebauer, P. et al. (2015). Fast Dictionary Matching for Content-Based Image Retrieval. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-19324-3_67
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19323-6
Online ISBN: 978-3-319-19324-3
eBook Packages: Computer ScienceComputer Science (R0)