Abstract
Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances). A plethora of approaches have been developed to solve this problem in the past years. Popular methods include the diverse density, MILIS and DD-SVM. While having been widely used, these methods, particularly those in computer vision have attempted fairly sophisticated solutions to solve certain unique and particular configurations of the MIL space. In this paper, we analyze the MIL feature space using modified versions of traditional non-parametric techniques like the Parzen window and k-nearest-neighbour, and develop a learning approach employing distances to k-nearest neighbours of a point in the feature space. We show that these methods work as well, if not better than most recently published methods on benchmark datasets. We compare and contrast our analysis with the well-established diverse-density approach and its variants in recent literature, using benchmark datasets including the Musk, Andrews' and Corel datasets, along with a diabetic retinopathy pathology diagnosis dataset. Experimental results demonstrate that, while enjoying an intuitive interpretation and supporting fast learning, these method have the potential of delivering improved performance even for complex data arising from real-world applications.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2605-2613 |
| Number of pages | 9 |
| Volume | 11-18-December-2015 |
| ISBN (Electronic) | 9781467383912 |
| DOIs | |
| State | Published - Feb 17 2016 |
| Event | 15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile Duration: Dec 11 2015 → Dec 18 2015 |
Other
| Other | 15th IEEE International Conference on Computer Vision, ICCV 2015 |
|---|---|
| Country | Chile |
| City | Santiago |
| Period | 12/11/15 → 12/18/15 |
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ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
Cite this
Simpler non-parametric methods provide as good or better results to multiple-instance learning. / Venkatesan, Ragav; Chandakkar, Parag Shridhar; Li, Baoxin.
Proceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015. Vol. 11-18-December-2015 Institute of Electrical and Electronics Engineers Inc., 2016. p. 2605-2613 7410656.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Simpler non-parametric methods provide as good or better results to multiple-instance learning
AU - Venkatesan, Ragav
AU - Chandakkar, Parag Shridhar
AU - Li, Baoxin
PY - 2016/2/17
Y1 - 2016/2/17
N2 - Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances). A plethora of approaches have been developed to solve this problem in the past years. Popular methods include the diverse density, MILIS and DD-SVM. While having been widely used, these methods, particularly those in computer vision have attempted fairly sophisticated solutions to solve certain unique and particular configurations of the MIL space. In this paper, we analyze the MIL feature space using modified versions of traditional non-parametric techniques like the Parzen window and k-nearest-neighbour, and develop a learning approach employing distances to k-nearest neighbours of a point in the feature space. We show that these methods work as well, if not better than most recently published methods on benchmark datasets. We compare and contrast our analysis with the well-established diverse-density approach and its variants in recent literature, using benchmark datasets including the Musk, Andrews' and Corel datasets, along with a diabetic retinopathy pathology diagnosis dataset. Experimental results demonstrate that, while enjoying an intuitive interpretation and supporting fast learning, these method have the potential of delivering improved performance even for complex data arising from real-world applications.
AB - Multiple-instance learning (MIL) is a unique learning problem in which training data labels are available only for collections of objects (called bags) instead of individual objects (called instances). A plethora of approaches have been developed to solve this problem in the past years. Popular methods include the diverse density, MILIS and DD-SVM. While having been widely used, these methods, particularly those in computer vision have attempted fairly sophisticated solutions to solve certain unique and particular configurations of the MIL space. In this paper, we analyze the MIL feature space using modified versions of traditional non-parametric techniques like the Parzen window and k-nearest-neighbour, and develop a learning approach employing distances to k-nearest neighbours of a point in the feature space. We show that these methods work as well, if not better than most recently published methods on benchmark datasets. We compare and contrast our analysis with the well-established diverse-density approach and its variants in recent literature, using benchmark datasets including the Musk, Andrews' and Corel datasets, along with a diabetic retinopathy pathology diagnosis dataset. Experimental results demonstrate that, while enjoying an intuitive interpretation and supporting fast learning, these method have the potential of delivering improved performance even for complex data arising from real-world applications.
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U2 - 10.1109/ICCV.2015.299
DO - 10.1109/ICCV.2015.299
M3 - Conference contribution
VL - 11-18-December-2015
SP - 2605
EP - 2613
BT - Proceedings - 2015 IEEE International Conference on Computer Vision, ICCV 2015
PB - Institute of Electrical and Electronics Engineers Inc.
ER -