Batch mode Adaptive Multiple Instance Learning for computer vision tasks

Publication Type:
Conference Proceeding
Citation:
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 2368 - 2375
Issue Date:
2012-10-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2013004300OK.pdf976.95 kB
Adobe PDF
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance labels in positive bags, the training process of traditional MIL methods is usually computationally expensive, which limits the applications of MIL in more computer vision tasks. In this paper, we propose a novel batch mode framework, namely Batch mode Adaptive Multiple Instance Learning (BAMIL), to accelerate the instance-level MIL methods. Specifically, instead of using all training bags at once, we divide the training bags into several sets of bags (i.e., batches). At each time, we use one batch of training bags to train a new classifier which is adapted from the latest pre-learned classifier. Such batch mode framework significantly accelerates the traditional MIL methods for large scale applications and can be also used in dynamic environments such as object tracking. The experimental results show that our BAMIL is much faster than the recently developed MIL with constrained positive bags while achieves comparable performance for text-based web image retrieval. In dynamic settings, BAMIL also achieves the better overall performance for object tracking when compared with other online MIL methods. © 2012 IEEE.
Please use this identifier to cite or link to this item: