Abstract
Multiple instance classification (MIC) is a kind of supervised learning, where data are represented as bags and each bag contains many instances. Training bags are given a label and the system tries to learn how to label unknown bags, without necessarily learning how to label individually each of their instances. In particular, we apply concepts drawn from MIC to the realm of content-based image retrieval, where images are described as bags of visual local descriptors. We introduce several classifiers, according to the different MIC paradigms, and evaluate them experimentally on a real-world dataset, comparing their accuracy and efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Actually, we removed the instance from the training set if its NN in a different bag is in a different class. This was required because it could happen that the NN of an instance belongs to the same bag.
References
Amato, G., Falchi, F.: kNN based image classification relying on local feature similarity. In: SISAP 2010 (2010)
Amores, J.: Multiple instance classification: review, taxonomy and comparative study. Artif. Intell. 201, 81–105 (2013)
Ardizzoni, S., Bartolini, I., Patella, M.: Windsurf: region-based image retrieval using wavelets. In: IWOSS 1999 (1999)
Bartolini, I., Ciaccia, P.: Imagination: exploiting link analysis for accurate image annotation. In: Boujemaa, N., Detyniecki, M., Nürnberger, A. (eds.) AMR 2007. LNCS, vol. 4918, pp. 32–44. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79860-6_3
Bartolini, I., Ciaccia, P., Patella, M.: Adaptively browsing image databases with PIBE. MTAP 31(3), 269–286 (2006)
Bartolini, I., Patella, M.: Windsurf: the best way to SURF (and SIFT/BRISK/ORB/FREAK, too). Multimedia Syst. 24(4), 459–476 (2018)
Bartolini, I., Patella, M., Stromei, G.: The windsurf library for the efficient retrieval of multimedia hierarchical data. In: SIGMAP 2011 (2011)
Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. TPAMI 24(24), 509–522 (2002)
Chen, Y., Bi, J., Wang, J.Z.: MILES: multiple-instance learning via embedded instance selection. TPAMI 28(12), 1931–1947 (2006)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. Comput. Surv. 40(2), 5 (2008)
Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: an experimental comparison. Inf. Retrieval 11(2), 77–107 (2008)
Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artif. Intell. 89(1–2), 31–71 (1997)
Foulds, J., Frank, E.: A review of multi-instance learning assumptions. Knowl. Eng. Rev. 25(1), 1–25 (2010)
Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: state of the art and challenges. TMCCA 2(1), 1–19 (2006)
Ling, H., Okada, K.: An efficient earth mover’s distance algorithm for robust histogram comparison. TPAMI 29(5), 840–853 (2007)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Maron, O., Ratan, A.L.: Multiple-instance learning for natural scene classification. In: ICML 1998 (1998)
Ratan, A.L., Maron, O., Grimson, W.E.L., Lozano-Pérez, T.: A framework for learning query concepts in image classification. In: CVPR 1999 (1999)
Rubner, Y., Tomasi, C.: Perceptual Metrics for Image Database Navigation. Springer, Boston (2013). https://doi.org/10.1007/978-1-4757-3343-3
Wu, J., Yu, Y., Huang, C., Yu, K.: Deep multiple instance learning for image classification and auto-annotation. In: CVPR 2015 (2015)
Yang, C., Lozano-Pérez, T.: Image database retrieval with multiple-instance learning techniques. In: ICDE 2000 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bartolini, I., Pascarella, P., Patella, M. (2019). Multiple Instance Classification in the Image Domain. In: Amato, G., Gennaro, C., Oria, V., Radovanović , M. (eds) Similarity Search and Applications. SISAP 2019. Lecture Notes in Computer Science(), vol 11807. Springer, Cham. https://doi.org/10.1007/978-3-030-32047-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-32047-8_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32046-1
Online ISBN: 978-3-030-32047-8
eBook Packages: Computer ScienceComputer Science (R0)