Object Detection with Bootstrapped Learning ∗

This paper proposes a novel framework for learning object detection without labeled training data. The basic idea is to avoid the time consuming task of hand labeling training samples by using large amounts of unsupervised data which is usually available in vision (e.g. a video stream). We propose a bootstrap approach which starts with a very simple object detection approach, the data obtained is fed to the next level which uses a robust learning mechanism to obtain a better object detector. If necessary this detector can be further improved by the same mechanism at the next level. We demonstrate this approach on a complex person detection task. We show that we can train a person model without any labeled training data.

On-line conservative learning for person detection

We present a novel on-line conservative learning framework for an object detection system. All algorithms operate in an on-line mode, in particular we also present a novel on-line AdaBoost method. The basic idea is to start with a very simple object detection system and to exploit a huge amount of unlabeled video data by being very conservative in selecting training examples. The key idea is to use reconstructive and discriminative classifiers in an iterative co-training fashion to arrive at increasingly better object detectors. We demonstrate the framework on a surveillance task where we learn person detectors that are tested on two surveillance video sequences. We start with a simple moving object classifier and proceed with incremental PCA (on shape and appearance) as a reconstructive classifier which in turn generates a training set for a discriminative on-line AdaBoost classifier. 1

Incremental LDA Learning by Combining Reconstructive and Discriminative Approaches

Incremental subspace methods have proven to enable efficient training if large amounts of training data have to be processed or if not all data is available in advance. In this paper we focus on incremental LDA learning which provides good classification results while it assures a compact data representation. In contrast to existing incremental LDA methods we additionally consider reconstructive information when incrementally building the LDA subspace. Hence, we get a more flexible representation that is capable to adapt to new data. Moreover, this allows to add new instances to existing classes as well as to add new classes. The experimental results show that the proposed approach outperforms other incremental LDA methods even approaching classification results obtained by batch learning.

Robot George - Interactive Continuous Learning of Visual Concepts

Abstract-The video presents the robot George learning visual concepts in dialogue with a tutor