Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification

We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture characteristics in a patch-based matching methodology. We introduce a novel criteria for selecting meaningful and discriminative exemplar patches. We optimize this function during training using a greedy submodular function optimization framework that gives a near-optimal solution with bounded approximation error. We use these selected exemplars as a dictionary basis and propose a spatially-aware sparse coding method to match testing images for identification while maintaining global shape correspondence. To accelerate the coding process for fast matching, we introduce a relaxed form that uses spatially-aware soft-thresholding during coding. Finally, we carry out an experimental study that demonstrates the effectiveness and efficiency of our exemplar selection and classification mechanisms, achieving $86.13\%$ accuracy on a difficult fine-grained species classification task distinguishing three types of fossil spruce pollen.Comment: CVMI 201

Learning preferences for large scale multi-label problems

Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable. The main contribution of this work is the proposal of a novel general online preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task

Performance Scalability in Communication Networks.

Performance scalability is an essential problem in modern communication networks that expand rapidly. In this dissertation, we consider three models of large-scale communication networks with limited local resources and investigate their asymptotic characteristics as the number of users or the size of the network increases. First, the effectiveness of application-layer coding in a network with a large number of users is considered. The end users encode data packets before transmitting them. The effect of additional packets on the network performance is twofold: (i) additional packets increase offered load, which results in higher drop probability, and (ii) some of dropped packets can be recovered at the receivers after decoding. It is argued that the space of all networks can be partitioned into two regions where coding is beneficial and detrimental, respectively. In particular, we establish an asymptotic regime that contains the boundary between these two regions. On the boundary, networks with and without coding have the same performance. Informally, application-layer coding improves the performance only in networks with low loss probabilities (without coding), and employing such coding in networks with high loss probabilities only degrades the performance. Next, we consider a k-node linear network consisting of bufferless nodes. The asymptotic behavior of the departure process is investigated, as the size of the network increases. Our result provides a complete characterization of a properly scaled limiting departure process, i.e., the joint probability density function of any finite number of consecutive inter-departure times, as the size of the network increases. Finally, linear networks consisting of finite-buffer nodes are considered, and properties of the throughput are investigated, as the size of the network increases. Using an approximation, we establish an asymptotic critical loading regime in which the ratio of the throughput to the input arrival rate is strictly within (0, 1). Such a regime is desirable from the point of view of both the throughput and network cost. Our results indicate that the qualitative behavior of the achievable throughput under the critical regime depends on whether the buffer size is greater than 1.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91568/1/cygene_1.pd