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» Learning Mid-Level Features For Recognition
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JMLR
2010
206views more  JMLR 2010»
13 years 4 months ago
Learning Translation Invariant Kernels for Classification
Appropriate selection of the kernel function, which implicitly defines the feature space of an algorithm, has a crucial role in the success of kernel methods. In this paper, we co...
Sayed Kamaledin Ghiasi Shirazi, Reza Safabakhsh, M...
ICCV
2007
IEEE
14 years 11 months ago
How Good are Local Features for Classes of Geometric Objects
Recent work in object categorization often uses local image descriptors such as SIFT to learn and detect object categories. Such descriptors explicitly code local appearance and h...
Michael Stark, Bernt Schiele
IBPRIA
2007
Springer
14 years 3 months ago
Unidimensional Multiscale Local Features for Object Detection Under Rotation and Mild Occlusions
Abstract. In this article, scale and orientation invariant object detection is performed by matching intensity level histograms. Unlike other global measurement methods, the presen...
Michael Villamizar, Alberto Sanfeliu, Juan Andrade...
ACL
2003
13 years 11 months ago
Syntactic Features and Word Similarity for Supervised Metonymy Resolution
We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-mo...
Malvina Nissim, Katja Markert
AAAI
2012
12 years 11 hour ago
A Testbed for Learning by Demonstration from Natural Language and RGB-Depth Video
We are developing a testbed for learning by demonstration combining spoken language and sensor data in a natural real-world environment. Microsoft Kinect RGBDepth cameras allow us...
Young Chol Song, Henry A. Kautz