| Summary: | 碩士 === 國立臺灣大學 === 電子工程學研究所 === 104 === Machine learning has received much attention in the computer vision community in the past few years and is involved various applications. Many future application such as home-care surveillance, intelligent agent and robotics become more and more popular in recent year.
However, there are still lots of limitations to apply the machine learning techniques into real-world learning scenario. Most of the current visual learning algorithm are dealing with static recognition problem, assuming that the numbers of categories and the training data are fixed. Another problem is that the recognition system can not handle the unseen category. To learn the new knowledge, it is costly to retrain the whole system each time when a new category is presented. Therefore, we need to figure out a way to make the robotic system learn incrementally and efficiently.
In this thesis, a novel incremental learning algorithm are presented. Our incremental learning system is based on SVM learning model and learns new classes in online scenario. We propose a novel incremental strategy to extend our model, and we learn with Learning Vectors, which is proposed to select the representative samples for incremental learning and can largely reduce the data storage. In addition, we also adopt online training techniques in our learning algorithm to learn the streaming data efficiently. In the end, we present the hardware architecture design for our learning system.
With the acceleration on training process, the system can deal with new knowledge instantly and it is suitable for many real-world visual learning applications such as human action recognition and multiple object tracking. To sum up, we propose a SVM-based incremental learning system which can learn incrementally and largely reduce the memory with acceptable decease in accuracy comparing with retraining the whole system.
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