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» Probabilistic Location Recognition using Reduced Feature Set
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KDD
2000
ACM
149views Data Mining» more  KDD 2000»
13 years 11 months ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar
MIR
2010
ACM
167views Multimedia» more  MIR 2010»
14 years 2 months ago
Improving automatic music classification performance by extracting features from different types of data
This paper discusses two sets of automatic musical genre classification experiments. Promising research directions are then proposed based on the results of these experiments. The...
Cory McKay, Ichiro Fujinaga
ATAL
2008
Springer
13 years 10 months ago
Coordination in ambiguity: coordinated active localization for multiple robots
In environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in multiple hypotheses...
Shivudu Bhuvanagiri, K. Madhava Krishna, Supreeth ...
ECCV
2008
Springer
14 years 9 months ago
Object Recognition by Integrating Multiple Image Segmentations
The joint tasks of object recognition and object segmentation from a single image are complex in their requirement of not only correct classification, but also deciding exactly whi...
Caroline Pantofaru, Cordelia Schmid, Martial Heber...
PSIVT
2009
Springer
400views Multimedia» more  PSIVT 2009»
14 years 2 months ago
Local Image Descriptors Using Supervised Kernel ICA
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
Masaki Yamazaki, Sidney Fels