Augmenting the Creation of 3D Character Motion By Learning from Video Data

When it comes to character motions, especially articulated character animation, the majority of efforts are spent on accurately capturing the low level and high level action styles. Among the many techniques which have evolved over the years, motion capture (mocap) and key frame animations are the two popular choices. Both techniques are capable of capturing the low level and high level action styles of a particular individual, but at great expense in terms of the human effort involved. In this thesis, we make use of performance data in video format to augment the process of character animation, considerably decreasing human effort for both style preservation and motion regeneration. Two new methods, one for high-level and another for low-level character animation, which are based on learning from video data to augment the motion creation process, constitute the major contribution of this research. In the first, we take advantage of the recent advancements in the field of action recognition to automatically recognize human actions from video data. High level action patterns are learned and captured using Hidden Markov Models (HMM) to generate action sequences with the same pattern. For the low level action style, we present a completely different approach that utilizes user-identified transition frames in a video to enhance the transition construction in the standard motion graph technique for creating smooth action sequences. Both methods have been implemented and a number of results illustrating the concept and applicability of the proposed approach are presented

Finding usage patterns from generalized weblog data

Buried in the enormous, heterogeneous and distributed information, contained in the web server access logs, is knowledge with great potential value. As websites continue to grow in number and complexity, web usage mining systems face two significant challenges - scalability and accuracy. This thesis develops a web data generalization technique and incorporates it into the web usage mining framework in an attempt to exploit this information-rich source of data for effective and efficient pattern discovery. Given a concept hierarchy on the web pages, generalization replaces actual page-clicks with their general concepts. Existing methods do this by taking a level-based cut through the concept hierarchy. This adversely affects the quality of mined patterns since, depending on the depth of the chosen level, either significant pages of user interests get coalesced, or many insignificant concepts are retained. We present a usage driven concept ascension algorithm, which only preserves significant items, possibly at different levels in the hierarchy. Concept usage is estimated using a small stratified sample of the large weblog data. A usage threshold is then used to define the nodes to be pruned in the hierarchy for generalization. Our experiments on large real weblog data demonstrate improved performance in terms of quality and computation time of the pattern discovery process. Our algorithm yields an effective and scalable tool for web usage mining

Quality analyses and improvement for fuzzy clustering and web personalization

Web mining researchers and practitioners keep on innovating and creating new technologies to help web site managers efficiently improve their offered web-based services and to facilitate information retrieval by web site users. The increasing amount of information and services offered through the Web coupled with the increase in web-based transactions calls for systems that can handle gigantic amount of usage information efficiently while providing good predictions or recommendations and personalization of web sites. In this thesis we first focus on clustering to obtain usage model from weblog data and investigate ways to improve the clustering quality. We also consider applications and focus on generating predictions through collaborative filtering which matches behavior of a current user with that of past like-minded users. To provide dependable performance analysis and improve clustering quality, we study 4 fuzzy clustering algorithms and compare their effectiveness and efficiency in web prediction. Dependability aspects led us further to investigate objectivity of validity indices and choose a more objective index for assessing the relative performance of the clustering techniques. We also use appropriate statistical testing methods in our experiments to distinguish real differences from those that may be due to sampling or other errors. Our results reconfirm some of the claims made previously about these clustering and prediction techniques, while at the same time suggest the need to assess both cluster validation and prediction quality for a sound comparison of the clustering techniques. To assess quality of aggregate usage profiles (UP), we devised a set of criteria which reflect the semantic characterization of UPs and help avoid resorting to subjective human judgment in assessment of UPs and clustering quality. We formulate each of these criteria as a computable measure for individual as well as for groups of UPs. We applied these criteria in the final phase of fuzzy clustering. The soundness and usability of the criteria have been confirmed through a user survey