Π-Avida -A Personalized Interactive Audio and Video Portal

Abstract We describe a system for enregistering, storing and distributing multimedia data streams. For each modality -audio, speech, video -characteristic features are extracted and used to classify the content into a range of topic categories. Using data mining techniques classifier models are determined from training data. These models are able to assign existing and new multimedia documents to one or several topic categories. We describe the features used as inputs for these classifiers. We demonstrate that the classification of audio material may be improved by using phonemes and syllables instead of words. Finally we show that the categorization performance mainly depends on the quality of speech recognition and that the simple video features we tested are of only marginal utility

R-Forest for Approximate Nearest Neighbor Queries in High Dimensional Space

Searching high dimensional space has been a challenge and an area of intense research for many years. The dimensionality curse has rendered most existing index methods all but useless causing people to research other techniques. In my dissertation I will try to resurrect one of the best known index structures, R-Tree, which most have given up on as a viable method of answering high dimensional queries. I have pointed out the various advantages of R-Tree as a method for answering approximate nearest neighbor queries, and the advantages of locality sensitive hashing and locality sensitive B-Tree, which are the most successful methods today. I started by looking at improving the maintenance of R-Tree by the use of bulk loading and insertion. I proposed and implemented a new method that bulk loads the index which was an improvement of standard method. I then turned my attention to nearest neighbor queries, which is a much more challenging problem especially in high dimensional space. Initially I developed a set of heuristics, easily implemented in R-Tree, which improved the efficiency of high dimensional approximate nearest neighbor queries. To further refine my method I took another approach, by developing a new model, known as R-Forest, which takes advantage of space partitioning while still using R-Tree as its index structure. With this new approach I was able to implement new heuristics and can show that R-Forest, comprised of a set of R-Trees, is a viable solution tohigh dimensional approximate nearest neighbor queries when compared to established methods