Investigation of segmentation based pooling for image quantification

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.

[1]  Christoph H. Lampert,et al.  Learning to Localize Objects with Structured Output Regression , 2008, ECCV.

[2]  Sebastian Nowozin,et al.  Structured Learning and Prediction in Computer Vision , 2011, Found. Trends Comput. Graph. Vis..

[3]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[4]  Mário A. T. Figueiredo,et al.  Image super-segmentation: Segmentation with multiple labels from shuffled observations , 2011, 2011 18th IEEE International Conference on Image Processing.

[5]  Zhuowen Tu,et al.  Image Parsing: Unifying Segmentation, Detection, and Recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[6]  Neal R. Harvey,et al.  Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction , 2002, IEEE Trans. Geosci. Remote. Sens..

[7]  Cristian Sminchisescu,et al.  Semantic Segmentation with Second-Order Pooling , 2012, ECCV.

[8]  Frédéric Jurie,et al.  Combining appearance models and Markov Random Fields for category level object segmentation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Andrew Zisserman,et al.  Deep Fisher Networks for Large-Scale Image Classification , 2013, NIPS.

[10]  Philip H. S. Torr,et al.  What, Where and How Many? Combining Object Detectors and CRFs , 2010, ECCV.

[11]  Martial Hebert,et al.  Discriminative random fields: a discriminative framework for contextual interaction in classification , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Zhuowen Tu,et al.  Graph-shifts: Natural image labeling by dynamic hierarchical computing , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Neal R. Harvey,et al.  Interactive image quantification tools in nuclear material forensics , 2011, Electronic Imaging.

[14]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  A. Cardona,et al.  An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy , 2010, PLoS biology.

[17]  Pushmeet Kohli,et al.  P3 & Beyond: Solving Energies with Higher Order Cliques , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Jiebo Luo,et al.  Learning multi-label scene classification , 2004, Pattern Recognit..

[19]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.

[20]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  H. Sebastian Seung,et al.  Maximin affinity learning of image segmentation , 2009, NIPS.

[22]  Camille Couprie,et al.  Power Watershed: A Unifying Graph-Based Optimization Framework , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Cordelia Schmid,et al.  Combining Regions and Patches for Object Class Localization , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[24]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[25]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[26]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  NowozinSebastian,et al.  Structured Learning and Prediction in Computer Vision , 2011 .

[28]  Srinivas C. Turaga,et al.  Machines that learn to segment images: a crucial technology for connectomics , 2010, Current Opinion in Neurobiology.

[29]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[30]  Joost van de Weijer,et al.  Harmony Potentials , 2011, International Journal of Computer Vision.

[31]  Kpalma Kidiyo,et al.  A Survey of Shape Feature Extraction Techniques , 2008 .

[32]  Jamie Shotton,et al.  The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[33]  Christy E. Ruggiero,et al.  Learning to merge: a new tool for interactive mapping , 2013, Defense, Security, and Sensing.

[34]  LeCunYann,et al.  Learning Hierarchical Features for Scene Labeling , 2013 .