Segmentation and Classification

1. HMM Model Selection Issues For Soccer Video Mark Baillie, Joemon M. Jose and Keith van Rijsbergen Department of Computing Science University of Glasgow bailliem, jj @dcs.gla.ac.uk http://ir.dcs.gla.ac.uk Overview:This poster describes an investigation into the effect of HMM parameter selection on system performance, for broad general audio classes found in Soccer video. Motivation There has been a concerted effort from the Video Retrieval community to provide tools that automate the annotation process of Sports video programmes. A popular indexing framework is the Hidden Markov Model (HMM). HMM is however largely applied in an ad hoc manner. The main thrust of our research is to provide an in-depth investigation into HMM parameter selection and what effect, if any, poor selection can have on indexing performance, specifically when modelling audio information. Such optimally selected models are then combined into a unified HMM framework for segmentation and classification of Soccer video. • HMM Selection Strategies • The main goal of model selection is to choose the simplest possible model without a deterioration in accuracy. This is important given the difficulty and practicality of generating large and varied training sets. For example, complex models require large training sets, while simpler models will not encapsulate content. • We investigate three model selection strategies and what effect each has on classification performance. • The three selection strategies are: • Exhaustive Search • Akaike Information Criterion (AIC) • Bayesian Information Criterion (BIC) • For all three strategies, the predictive likelihood score is used as a guide for HMM selection. As the number of hidden Markov states increase so does the classification accuracy, levelling off once a specific number of states is added. A good model selection strategy will pick a HMM at this point. A simpler Gaussian Mixture Model (GMM) is displayed as a baseline. As the parameter increases so does the likelihood score. For Exhaustive Search,a threshold is required to identify the optimal model Both the AIC and BIC selection criterion penalise the predictive likelihood score, creating a maximum peak. We assume this maxima to be the optimal HMM. Model Selection Results • We compared selection strategies across a large test collection, using three broad content classes found in Soccer video. • A simple GMM classifier was used as a baseline • BIC selected the simplest HMMs • No significant improvement across strategies • No significant improvement over baseline GMM • Segmentation and Classification • Optimally selected HMMs using BIC were integrated into a Superior HMM framework • A Soccer video topology was generated utilising domain knowledge • Each state in the Superior HMM topology represented specific content found in a Soccer video file • Able to incorporate the temporal flow of the video file into the segmentation process • Segment and classify an entire video file into semantic units in a single pass • Conclusions • We highlighted the importance of model selection from experimentation • Selecting too few or too many hidden states can produce poor classification accuracy • Selection of complex models will resulting in training and over-fitting problems • We found BIC to select the simplest optimal HMMs without effecting accuracy • Optimally selected HMMs can then be integrated into a segmentation and classification system for Soccer video