Changes

I will present a framework for the automatic recognition of complex multi-agent events in settings where structure is imposed by rules that agents must follow while performing activities.  Given semantic spatio-temporal descriptions of what generally happens (i.e., rules, event descriptions, physical constraints), and based on video analysis, the framework determines the events that occurred.  Knowledge about spatio-temporal structure is encoded using first-order logic using an approach based on Allen's Interval Logic, and robustness to low-level observation uncertainty is provided by Markov Logic Networks (MLN).  The main contribution is that the framework integrates interval-based temporal reasoning with probabilistic logical inference, relying on an efficient bottom-up grounding scheme to avoid combinatorial explosion. Applied to one-on-one basketball, the framework detects and tracks players, their hands and feet, and the ball, generates event observations from the resulting trajectories, and performs probabilistic logical inference to determine the most consistent sequence of events.

I will present a framework for the automatic recognition of complex multi-agent events in settings where structure is imposed by rules that agents must follow while performing activities.  Given semantic spatio-temporal descriptions of what generally happens (i.e., rules, event descriptions, physical constraints), and based on video analysis, the framework determines the events that occurred.  Knowledge about spatio-temporal structure is encoded using first-order logic using an approach based on Allen's Interval Logic, and robustness to low-level observation uncertainty is provided by Markov Logic Networks (MLN).  The main contribution is that the framework integrates interval-based temporal reasoning with probabilistic logical inference, relying on an efficient bottom-up grounding scheme to avoid combinatorial explosion. Applied to one-on-one basketball, the framework detects and tracks players, their hands and feet, and the ball, generates event observations from the resulting trajectories, and performs probabilistic logical inference to determine the most consistent sequence of events.

===A Vision System to Extract "Simple" Objects in a Purely Bottom-Up Fashion===

===A Vision System to Extract "Simple" Objects in a Purely Bottom-Up Fashion===

Also, to understand the role of fixation in perception, Ajay recommends taking the psychophysical test available at http://www.umiacs.umd.edu/~mishraka/fixationExperiment.php

Also, to understand the role of fixation in perception, Ajay recommends taking the psychophysical test available at http://www.umiacs.umd.edu/~mishraka/fixationExperiment.php

===Fast Imaging with Slow Cameras===

===Fast Imaging with Slow Cameras===

The utility of context for supervised object recognition has been well acknowledged from the early seventies, and has been practically demonstrated by many systems in the last few years. The goal of this talk is to understand the role of context in unsupervised pattern identification scenarios. We consider two problems of clustering a set of unlabelled data points using maximum margin principles, and adapting a classifier trained on a specific domain to identify instances across novel domain shifting transformations, and propose contextual sources that provide pertinent information on the identity of the unlabelled data.

The utility of context for supervised object recognition has been well acknowledged from the early seventies, and has been practically demonstrated by many systems in the last few years. The goal of this talk is to understand the role of context in unsupervised pattern identification scenarios. We consider two problems of clustering a set of unlabelled data points using maximum margin principles, and adapting a classifier trained on a specific domain to identify instances across novel domain shifting transformations, and propose contextual sources that provide pertinent information on the identity of the unlabelled data.

===Utilizing Contextual Information for Scene Understanding and Image Retrieval===

===Utilizing Contextual Information for Scene Understanding and Image Retrieval===

Next, I will present a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multi-word queries. Moreover, these approaches ignore the interdependencies among the query words. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval.

Next, I will present a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multi-word queries. Moreover, these approaches ignore the interdependencies among the query words. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval.

===Robust Regression Using Sparse Learning===

===Robust Regression Using Sparse Learning===