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Evaluation and Comparison of Inferred Regular Grammars
 In Proceedings of the International Colloquium on Grammar Inference (ICGI
, 2008
"... Abstract. The accuracy of an inferred grammar is commonly computed by measuring the percentage of sequences that are correctly classified from a random sample of sequences produced by the target grammar. This approach is problematic because (a) it is unlikely that a random sample of sequences will a ..."
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Abstract. The accuracy of an inferred grammar is commonly computed by measuring the percentage of sequences that are correctly classified from a random sample of sequences produced by the target grammar. This approach is problematic because (a) it is unlikely that a random sample of sequences will adequately test the grammar and (b) the use of a single probability value provides little insight into the extent to which a grammar is (in)accurate. This paper addresses these two problems by proposing the use of established modelbased testing techniques from the field of software engineering to systematically generate test sets, along with the use of the Precision and Recall measure from the field of information retrieval to concisely represent the accuracy of the inferred machine.
Exemplarbased Robust Coherent Biclustering
"... The biclustering, coclustering, or subspace clustering problem involves simultaneously grouping the rows and columns of a data matrix to uncover biclusters or submatrices of the data matrix that optimize a desired objective function. In coherent biclustering, the objective function contains a cohe ..."
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The biclustering, coclustering, or subspace clustering problem involves simultaneously grouping the rows and columns of a data matrix to uncover biclusters or submatrices of the data matrix that optimize a desired objective function. In coherent biclustering, the objective function contains a coherence measure of the biclusters. We introduce a novel formulation of the coherent biclustering problem and use it to derive two algorithms. The first algorithm is based on loopy message passing; and the second relies on a greedy strategy yielding an algorithm that is significantly faster than the first. A distinguishing feature of these algorithms is that they identify an exemplar or a prototypical member of each bicluster. We note the interference from background elements in biclustering, and offer a means to circumvent such interference using additional regularization. Our experiments with synthetic as well as realworld datasets show that our algorithms are competitive with the current stateoftheart algorithms for finding coherent biclusters. 1
Learning and Parsing Video Events with Goal and Intent Prediction
"... In this paper, we present a framework for parsing video events with stochastic Temporal AndOr Graph (TAOG) and unsupervised learning of the TAOG from video. This TAOG represents a stochastic event grammar. The alphabet of the TAOG consists of a set of grounded spatial relations including the po ..."
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In this paper, we present a framework for parsing video events with stochastic Temporal AndOr Graph (TAOG) and unsupervised learning of the TAOG from video. This TAOG represents a stochastic event grammar. The alphabet of the TAOG consists of a set of grounded spatial relations including the poses of agents and their interactions with objects in the scene. The terminal nodes of the TAOG are atomic actions which are specified by a number of grounded relations over image frames. An Andnode represents a sequence of actions. An Ornode represents a number of alternative ways of such concatenations. The AndOr nodes in the TAOG can generate a set of valid temporal configurations of atomic actions, which can be equivalently represented as a stochastic contextfree grammar (SCFG). For each Andnode we model the temporal relations of its children nodes to distinguish events with similar structures but different temporal patterns and interpolate missing portions of events. This makes the TAOG grammar contextsensitive. We propose an unsupervised learning algorithm to learn the atomic actions, the temporal relations and the AndOr nodes under the information projection principle in a coherent probabilistic framework. We also propose an event parsing algorithm based on the TAOG which can understand events, infer the goal of agents, and predict their plausible intended actions. In comparison with existing methods, our paper makes
Unsupervised Learning of Event ANDOR Grammar and Semantics from Video
"... We study the problem of automatically learning event ANDOR grammar from videos of a certain environment, e.g. an office where students conduct daily activities. We propose to learn the event grammar under the information projection and minimum description length principles in a coherent probabilist ..."
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We study the problem of automatically learning event ANDOR grammar from videos of a certain environment, e.g. an office where students conduct daily activities. We propose to learn the event grammar under the information projection and minimum description length principles in a coherent probabilistic framework, without manual supervision about what events happen and when they happen. Firstly a predefined set of unary and binary relations are detected for each video frame: e.g. agentâ€™s position, pose and interaction with environment. Then their cooccurrences are clustered into a dictionary of simple and transient atomic actions. Recursively these actions are grouped into longer and complexer events, resulting in a stochastic event grammar. By modeling time constraints of successive events, the learned grammar becomes contextsensitive. We introduce a new dataset of surveillancestyle video in office, and present a prototype system for video analysis integrating bottomup detection, grammatical learning and parsing. On this dataset, the learning algorithm is able to automatically discover important events and construct a stochastic grammar, which can be used to accurately parse newly observed video. The learned grammar can be used as a prior to improve the noisy bottomup detection of atomic actions. It can also be used to infer semantics of the scene. In general, the event grammar is an efficient way for common knowledge acquisition from video. 1.
Unsupervised Structure Learning of Stochastic AndOr Grammars
"... Stochastic AndOr grammars compactly represent both compositionality and reconfigurability and have been used to model different types of data such as images and events. We present a unified formalization of stochastic AndOr grammars that is agnostic to the type of the data being modeled, and propo ..."
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Stochastic AndOr grammars compactly represent both compositionality and reconfigurability and have been used to model different types of data such as images and events. We present a unified formalization of stochastic AndOr grammars that is agnostic to the type of the data being modeled, and propose an unsupervised approach to learning the structures as well as the parameters of such grammars. Starting from a trivial initial grammar, our approach iteratively induces compositions and reconfigurations in a unified manner and optimizes the posterior probability of the grammar. In our empirical evaluation, we applied our approach to learning event grammars and image grammars and achieved comparable or better performance than previous approaches. 1