Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weakly supervised object detector learning with model drift detection
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Scene recognition and weakly supervised object localization with deformable part-based models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We propose a weakly-supervised learning method for object detection using color and depth images of a real environment attached with object labels. The proposed method applies Multiple Instance Learning to find proper instances of the objects in training images. This method is novel in the sense that it learns multiple objects simultaneously in a way to balance the scores of each training sample across all object classes. Moreover, we combine 3D features considering different properties, that is, color texture, grayscale texture, and surface curvature, to improve the performance. We show that our method surpasses a conventional method using color and depth images. Furthermore, we evaluate its performance with our new dataset consisting of color and depth images with weak labels of 100 objects and various backgrounds.