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| October 6
| October 6
| Arpit Jain
| Arpit Jain
|
| Learning What and How of Contextual Models for Scene Labeling
|-
|-
| October 13
| October 13
===Scene Classification with Visual Filters===
===Scene Classification with Visual Filters===
Speaker: [http://www.cs.umd.edu/~dss/ Douglas Summers-Stay] --- Date: September 29, 2011
Speaker: [http://www.cs.umd.edu/~dss/ Douglas Summers-Stay] -- Date: September 29, 2011
"Scene Classification" is the computer vision problem of labeling all the pixels in an image according to the class they fall into, such as "street," "tree," or "person." A tool we have developed here at the computer vision lab called "visual filters" uses a series of nonlinear filters to attempt to create such classification maps. I will discuss what we are doing now and how we can incorporate ideas from "deep learning" to improve this in the future. An introduction for beginners with some examples is [http://llamasandmystegosaurus.blogspot.com/search?q=visual+filters here].
"Scene Classification" is the computer vision problem of labeling all the pixels in an image according to the class they fall into, such as "street," "tree," or "person." A tool we have developed here at the computer vision lab called "visual filters" uses a series of nonlinear filters to attempt to create such classification maps. I will discuss what we are doing now and how we can incorporate ideas from "deep learning" to improve this in the future. An introduction for beginners with some examples is [http://llamasandmystegosaurus.blogspot.com/search?q=visual+filters here].
===Learning What and How of Contextual Models for Scene Labeling===
Speaker: [http://www.umiacs.umd.edu/~ajain/ Arpit Jain] -- Date: October 6, 2011
In this talk I will discuss about a data-driven approach to predict the importance of edges and construct a Markov network for image analysis based on statistical models of global and local image features. Most of the previous approaches used either a fixed fully connected Markov Network(MN) or ad-hoc neighborhood connected MN. But not edges in MN are useful and this is what I will show during my talk. I will also address the coupled problem of predicting the feature weights associated with each edge of a Markov network for evaluation of context. Experimental results indicate that this scene dependent structure construction model eliminates spurious edges and improves performance over fully-connected and neighborhood connected Markov network.
