Study On Online Learning Algorithms In Video Concept Detection

In semantic concept detection of the online video streams, the underlying data distribution for a certain semantic concept in the visual feature space generally evolves over time. This thesis will tackle two key issues: i) what are the rules of the evolving underlying data distribution for different semantic concepts at different conditions? ii) how to update the concept models for the limited training samples from the current video sequence?The major contributions of this thesis comprise:1) Based on the Finite Mixture Models (FMMs), a couple of tracking measures are proposed to describe statistical properties of the evolving underlying data distribution in a quantitative way. On one hand, they can be utilized to investigate the evolving rules of different semantic concepts. On the other hand, they can provide much reasonable prior knowledge of the online data streams for the establishment of the whole online learning system.2) The Multi-granularity Adaptive (MGA) online learning algorithm in supervised learning is proposed. It mainly focuses on studying the statistical properties of the diverse granularity in the time domain for the different semantic concepts, and the corresponding classifiers fusion techniques. Two types of the classifier selection, the incremental version and the flat version, are both investigated in detail. In addition, the relationship between the enhancing capacity of the overall performance and the above defined tracking measures are also covered. This MGA algorithm is suitable for the situation where the current target concept evolves relatively quickly over time.3) The Online-optimized Incremental Learning (OOIL) algorithm in semi-supervised learning is proposed. It manages to sufficiently utilizes the statistical characteristics of the easily-collected unlabeled data samples from the newly-upcoming online data streams. The Local Adaptation (LA) step can derives the latest local concept models, which can also be used to dynamically update the original global concept models, resulting in the Global Model Incremental Updating (GMIU) step. Therefore, this algorithm has solved the problem of model updating in semi-supervised learning under appropriate conditions. This OOIL algorithm is suitable for the circumstance where the current target concept evolves relatively slowly over time.4) The experimental results show that, the proposed FMM-based tracking measures are very useful and practical to describe the evolving process of the underlying data distribution, and they are also able to be applied to the online learning applications in other domains. These tracking measures can effectively derive the evolving rule of the target concept, and provide much reasonable reference information for the above two types of the online learning algorithms. Furthermore, the experimental results based on the sports video and the large-scale TRECVID data collections demonstrate that, compared with the existing strategies, the two types of the online learning algorithms (MGA and OOIL) proposed in this thesis are more effective.