Friday, May 10, 2013 — 3:00 PM EDT

Speaker

Michael Yiu Ka Diu

Title

Image Analysis Applications of the Maximum Mean Discrepancy Distance Measure

Abstract

The need to quantify distance between two groups of objects is prevalent throughout the signal processing world. The Euclidean distance is one of the predominant distance measures used to compare feature vectors and groups of vectors, but many problems arise with it when high data dimensionality is present. Maximum mean discrepancy (MMD) is a modern unsupervised kernel-based pattern recognition method which may improve differentiation between two distinct populations over many commonly used methods such as the L2 distance, when paired with the proper feature representations and kernels. MMD-based distance computation combines many powerful concepts from the machine learning literature, such as data distribution-leveraging similarity measures and kernel methods for machine learning. Due to this heritage, we posit that dissimilarity-based classification and changepoint detection using MMD can lead to enhanced separation between different populations. To test this hypothesis, we conduct studies comparing MMD and the L2 distance in two subareas of image analysis and understanding: first, to detect scene changes in video in an unsupervised manner, and secondly, in the biomedical imaging arena, using clinical ultrasound to assess tumor response to treatment. We leverage modern computer vision data descriptors, such as the bag-of-visual-words and sparse combinations of SIFT descriptors, and choose from an assessment of several similarity kernels (e.g. Histogram Intersection, Radial Basis Function) in order to engineer useful systems using MMD. Promising improvements over L2, measured primarily using precision/recall for scene change detection, and k-nearest neighbor classification accuracy for tumor response assessment, are obtained in both applications.

Supervisor

Kamel, Mohamed S.

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