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Proceedings Paper

Investigation of segmentation based pooling for image quantification
Author(s): Reid Porter; Neal Harvey; Christy Ruggiero
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Paper Abstract

A key step in many image quantification solutions is feature pooling, where subsets of lower-level features are combined so that higher-level, more invariant predictions can be made. The pooling region, which defines the subsets, often has a fixed spatial size and geometry, but data-adaptive pooling regions have also been used. In this paper we investigate pooling strategies for the data-adaptive case and suggest a new framework for pooling that uses multiple sub-regions instead of a single region. We show that this framework can help represent the shape of the pooling region and also produce useful pairwise features for adjacent pooling regions. We demonstrate the utility of the framework in a number of classification tasks relevant to image quantification in digital microscopy.

Paper Details

Date Published: 7 March 2014
PDF: 11 pages
Proc. SPIE 9024, Image Processing: Machine Vision Applications VII, 90240F (7 March 2014); doi: 10.1117/12.2038707
Show Author Affiliations
Reid Porter, Los Alamos National Lab. (United States)
Neal Harvey, Los Alamos National Lab. (United States)
Christy Ruggiero, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 9024:
Image Processing: Machine Vision Applications VII
Kurt S. Niel; Philip R. Bingham, Editor(s)

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