Combining Local and Global Image Features for Object Class Recognition

Lisin, DA; Mattar, MA; Blaschko, MB; Benfield, MC; Learned-Miller, EG

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Combining Local and Global Image Features for Object Class Recognition

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1565348
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Lisin, D., Mattar, M., Blaschko, M., Benfield, M., & Learned-Miller, E. (2005). Combining Local and Global Image Features for Object Class Recognition. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05): Workshops (pp. 1-8). Los Alamitos, CA, USA: IEEE.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D55D-C

Abstract

Object recognition is a central problem in computer vision research. Most object recognition systems have taken one of two approaches, using either global or local features exclusively. This may be in part due to the difficulty of combining a single global feature vector with a set of local features in a suitable manner. In this paper, we show that combining local and global features is beneficial in an application where rough segmentations of objects are available. We present a method for classification with local features using non-parametric density estimation. Subsequently, we present two methods for combining local and global features. The first uses a "stacking" ensemble technique, and the second uses a hierarchical classification system. Results show the superior performance of these combined methods over the component classifiers, with a reduction of over 20% in the error rate on a challenging marine science application.