Fast Retrieval Algorithm Using EMD Lower and Upper Bounds and a Search Algorithm in multidimensional index

Comparison of images requires a distance metric that is sensitive to the spatial location of objects and features. The Earth Mover’s Distance was introduced in Computer Vision to better approach human perceptual similarities. Its computation, however, is too complex for usage in interactive multimedia database scenarios. We develop new upper bounding approximation techniques for the Earth Mover’s Distance which satisfy high quality criteria and fast computation. In order to enable efficient query processing in large databases, we propose an index structure LUBMTree (Lower and Upper Bounds MTree), based of using the lower and upper bounds for the EMD to improve the search time. Experiments show the performance of research in the  LUBMTree compared with those obtained by  the research in the MTree. Keywords : indexing, similarity, search, signature, metric EMD, MTree, MAM

Fast Retrieval Algorithm for Earth Mover's Distance Using EMD Lower Bounds and a Skipping Algorithm

The earth mover's distance (EMD) is a measure of the distance between two distributions, and it has been widely used in multimedia information retrieval systems, in particular, in content-based image retrieval systems. When the EMD is applied to image problems based on color or texture, the EMD reflects the human perceptual similarities. However, its computations are too expensive to use in large-scale databases. In order to achieve efficient computation of the EMD during query processing, we have developed “fastEMD,” a library for high-speed feature-based similarity retrievals in large databases. This paper introduces techniques that are used in the implementation of the fastEMD and performs extensive experiments to demonstrate its efficiency

Bayesian methods in music modelling

This thesis presents several hierarchical generative Bayesian models of musical signals designed to improve the accuracy of existing multiple pitch detection systems and other musical signal processing applications whilst remaining feasible for real-time computation. At the lowest level the signal is modelled as a set of overlapping sinusoidal basis functions. The parameters of these basis functions are built into a prior framework based on principles known from musical theory and the physics of musical instruments. The model of a musical note optionally includes phenomena such as frequency and amplitude modulations, damping, volume, timbre and inharmonicity. The occurrence of note onsets in a performance of a piece of music is controlled by an underlying tempo process and the alignment of the timings to the underlying score of the music. A variety of applications are presented for these models under differing inference constraints. Where full Bayesian inference is possible, reversible-jump Markov Chain Monte Carlo is employed to estimate the number of notes and partial frequency components in each frame of music. We also use approximate techniques such as model selection criteria and variational Bayes methods for inference in situations where computation time is limited or the amount of data to be processed is large. For the higher level score parameters, greedy search and conditional modes algorithms are found to be sufficiently accurate. We emphasize the links between the models and inference algorithms developed in this thesis with that in existing and parallel work, and demonstrate the effects of making modifications to these models both theoretically and by means of experimental results