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| November 20
| November 20
| Kota Hara
| Kota Hara
|
| Growing Regression Forests by Classification: Applications to Object Pose Estimation
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|-
| November 27
| November 27
Abstract: Collections of filters based on histograms of oriented gradients (HOG) are common for several detection methods, notably, poselets and exemplar SVMs. The main bottleneck in training such systems is the selection of a subset of good filters from a large number of possible choices. We show that one can learn a universal model of part “goodness” based on properties that can be computed from the filter itself. The intuition is that good filters across categories exhibit common traits such as, low clutter and gradients that are spatially correlated. This allows us to quickly discard filters that are not promising thereby speeding up the training procedure. Applied to training the poselet model, our automated selection procedure allows us to improve its detection performance on the PASCAL VOC data sets, while speeding up training by an order of magnitude. Similar results are reported for exemplar SVMs.
Abstract: Collections of filters based on histograms of oriented gradients (HOG) are common for several detection methods, notably, poselets and exemplar SVMs. The main bottleneck in training such systems is the selection of a subset of good filters from a large number of possible choices. We show that one can learn a universal model of part “goodness” based on properties that can be computed from the filter itself. The intuition is that good filters across categories exhibit common traits such as, low clutter and gradients that are spatially correlated. This allows us to quickly discard filters that are not promising thereby speeding up the training procedure. Applied to training the poselet model, our automated selection procedure allows us to improve its detection performance on the PASCAL VOC data sets, while speeding up training by an order of magnitude. Similar results are reported for exemplar SVMs.
===Growing Regression Forests by Classification: Applications to Object Pose Estimation===
Speaker: [http://www.kotahara.com/ Kota Hara] -- Date: November 20, 2014
Abstract: In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first finds clusters of the training data which at least locally minimize the empirical loss without considering the input space. Then splitting rules which preserve the found clusters as much as possible are determined by casting the problem into a classification problem. Consequently, our new node splitting method enjoys more freedom in choosing the splitting rules, resulting in more efficient tree structures. In addition to the Euclidean target space, we present a variant which can naturally deal with a circular target space by the proper use of circular statistics. We apply the regression forest employing our node splitting to head pose estimation (Euclidean target space) and car direction estimation (circular target space) and demonstrate that the proposed method significantly outperforms state-of-the-art methods (38.5\% and 22.5\% error reduction respectively).
==Past Semesters==
==Past Semesters==
