An empirical study on image bag generators for multi-instance learning

Machine Learning

Volumn 105, Issue 2, 2016, Pages 155-198

  Wei, Xiu Shen ^a   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

Bag generator; Empirical study; Image bag generators; Multi instance learning

Indexed keywords

CLASSIFICATION (OF INFORMATION);

BAG GENERATOR; DATA OBJECTS; DIVERSE APPLICATIONS; EMPIRICAL STUDIES; MULTI-INSTANCE LEARNING; PATCH SIZE; SAMPLING STRATEGIES; STATE OF THE ART;

LEARNING ALGORITHMS;

EID: 84961839061     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-016-5560-1     Document Type: Article

References (46)

1. Amores, J. (2013). Multiple instance classification: Review, taxonomy and comparative study. Artificial Intelligence, 201, 81–105.
2. Andrews, S., Tsochantaridis, I., & Hofmann, T. (2003). Support vector machines for multiple-instance learning. In Advances in neural information processing systems (Vol. 15, pp. 561–568). Cambridge, MA: MIT Press.
3. Carson, C., Belongie, S., Greenspan, H., & Malik, J. (2002). Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8), 1026–1038.
4. Chatfield, K., Lempitsky, V., Vedaldi, A., & Zisserman, A. (2011). The devil is in the details: An evaluation of recent feature encoding methods. In Proceedings of 22nd British machine vision conference. Dundee, Scotland, pp. 1–12.
5. Chen, S., Sista, S., Shyu, M., & Kashyap, R. L. (2000). An indexing and searching structure for multimedia database systems. In Proceedings of IS&T/SPIE conference storage and retrieval for media databases. San Jose, CA, pp. 262–270.
6. Chen, Y., Bi, J., & Wang, J. Z. (2006). MILES: Multiple-instance learning via embedded instance selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 1931–1947.
7. Chen, Y. X., & Wang, J. Z. (2004). Image categorization by learning and reasoning with regions. Journal of Machine Learning Research, 5, 913–939.
8. Chevaleyre, Y., & Zucker, J. D. (2001). A framework for learning rules from multiple instance data. In Proceedings of 12th European conference on machine learning. Freiburg, Germany, pp. 49–60.
9. Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.
10. Deng, Y. N., & Manjunath, B. S. (2001). Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8), 800–810.
11. Dietterich, T. G., Lathrop, R. H., & Lozano-Pérez, T. (1997). Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1–2), 31–71.
12. Felzenszwalb, P., Girshick, R., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627–1645.
13. Fisher, R. (1959). Statistical methods and scientific inference (2nd ed.). New York: Hafner Publishing Company.
14. Fung, G., Dundar, M., Krishnapuram, B., & Rao, R. B. (2007). Multiple instance learning for computer aided diagnosis. In Advances in neural information processing systems (Vol. 19, pp. 425–432). Cambridge, MA: MIT Press.
15. Heikkilä, M., & Pietikäinen, M. (2006). A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4), 657–662.
16. Leistner, C., Saffari, A., & Bischof, H. (2010). MIForests: Multiple-instance learning with randomized trees. In Proceedings of 11th European onference on computer vision. Crete, Greece, pp. 29–42.
17. Li, F. F., & Perona, P. (2005). A Bayesian hierarchical model for learning natural scene categories. In Proceedings of IEEE computer society conference on computer vision and pattern recognition. San Diego, CA, pp. 524–531.
18. Li, H., Wang, M., & Hua, X. S. (2009). MSRA-MM 2.0: A large-scale web multimedia dataset. In Proceedings of 9th international conference on data mining workshops. Miami, Florida, pp. 164–169.
19. Liu, W., Xu, W. D., Li, L. H., & Li, G. L. (2008). Two new bag generators with multi-instance learning for image retrieval. In Proceedings of 3rd IEEE conference on industrial electronics and applications. Singapore, pp. 255 – 259.
20. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.
21. Maron, O., & Lozano-Pérez, T. (1998). A framework for multiple-instance learning. In Advances in neural information processing systems (Vol. 10, pp. 570–576). Cambridge, MA: MIT Press.
22. Maron, O., & Ratan, A. L. (2001). Multiple-instance learning for natural scene classification. In Proceedings of 18th international conference on machine learning. Williamstown, MA, pp. 425–432.
23. Nemenyi, P. B. (1963) Distribution-free multiple comparisons. PhD thesis.
24. Nguyen, M. H., Torresani, L., Torre, F., & Rother, C. (2009). Weakly supervised discriminative localization and classification: A joint learning process. In Proceedings of 12th international conference on computer vision. Kyoto, Japan, pp. 1925–1932.
25. Nowak, E., Jurie, F., & Triggs, B. (2006). Sampling strategies for bag-of-features image classification. In Proceedings of 9th European conference on computer vision. Graz, Austria, pp. 490–503.
26. Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.
27. Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013). Image classification with the fisher vector: Theory and practice. International Journal of Computer Vision, 105(3), 222–245.
28. Settles, B., Craven, M., & Ray, S. (2008). Multiple instance active learning. In Advances in neural information processing systems (Vol. 20, pp. 1289–1296). Cambridge, MA: MIT Press.
29. Song, X. F., Jiao, L. C., Yang, S. Y., Zhang, X. R., & Shang, F. H. (2013). Sparse coding and classifier ensemble based multi-instance learning for image categorization. Signal Processing, 93, 1–11.
30. Tang, J. H., Li, H. J., Qi, G. J., & Chua, T. S. (2010). Image annotation by graph-based inference with integrated multiple/single instance representations. IEEE Transactions on Multimedia, 12, 131–141.
31. Vijayanarasimhan, S., & Grauman, K. (2008). Keywords to visual categories: Multiple-instance learning for weakly supervised object categorization. In Proceedings of IEEE computer society conference on computer vision and pattern recognition. Anchorage, Alaska, pp. 1–8.
32. Viola, P., Platt, J., & Zhang, C. (2006). Multiple instance boosting for object detection. In Advances in neural information processing systems (Vol. 18, pp. 1419–1426). Cambridge, MA: MIT Press.
33. Wang, J., & Zucker, J. D. (2000). Solving multiple-instance problem: A lazy learning approach. In Proceedings 17th international conference on machine learning. San Francisco, CA, pp. 1119–1125.
34. Wei, X. S., Wu, J., & Zhou, Z. H. (2014). Scalable multi-instance learning. In Proceedings of 14th international conference on data mining. Shenzhen, China, pp. 1037–1042.
35. Xu, X., & Frank, E. (2004). Logistic regression and boosting for labeled bags of instances. In Proceedings of 8th Pacific-Asia conference on knowledge discovery and data mining. Sydney, Australia, pp. 272–281.
36. Xu, Y. Y., & Shih, C. H. (2012). Content based Image retrieval using multiple instance decision based neural networks. In Proceedings of IEEE international conference on computational intelligence and cybernetics. Bali, Indonesia, pp. 175–179.
37. Yang, C., & Lozano-Pérez, T. (2000). Image database retrieval with multiple-instance learning techniques. In Proceedings of 16th international conference on data engineering. San Diego, CA, pp. 233–243.
38. Yang, C. B., Dong, M., & Hua, J. (2006). Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning. In Proceedings of IEEE computer society conference on computer vision and pattern recognition. New York, NY, pp. 2057–2063.
39. Zhang, C., & Viola, P. (2008). Multi-instance learning pruning for learning efficient cascade detectors. In Advances in neural information processing systems (Vol. 20, pp. 1681–1688). Cambridge, MA: MIT Press.
40. Zhang, C. C., Chen, S., & Shyu, M. (2004). Multiple object retrieval for image databases using multiple instance learning and relevance feedback. In Proceedings of IEEE international conference on multimedia and expo. Sydney, Australia, pp. 775–778.
41. Zhang, Q., & Goldman, S. A. (2000). EM-DD: An improved multiple-instance learning technique. In Proceedings of 16th IEEE international conference on data engineering. San Diego, California, USA, pp. 233–243.
42. Zhang, Q., Goldman, S. A., Yu, W., & Fritts, J. E. (2002). Content-based image retrieval using multiple-instance learning. In Proceedings of 19th international conference on machine learning. Sydney, Australia, pp. 682–689.
43. Zhou, Z. H., Zhang, M. L., & Chen, K. J. (2003). A novel bag generator for image database retrieval with multi-instance learning. In Proceedings of 15th IEEE international conference on tools with artificial intelligence. Sacramento, CA, pp. 565–569.
44. Zhou, Z. H., Jiang, K., & Li, M. (2005). Multi-instance learning based web mining. Applied Intelligence, 22(2), 135–147.
45. Zhou, Z. H., Sun, Y. Y., & Li, Y. F. (2009). Multi-instance learning by treating instances as non-I.I.D. samples. In Proceedings of 26th international conference on machine learning. Montreal, Canada, pp. 1249–1256.
46. Zhu, S. L., & Tan, X. Q. (2011). A novel automatic image annotation method based on multi-instance learning. Procedia Engineering, 15, 3439–3444.