Bio-mimetic learning from images using imprecise expert information

We present a method for training a cross-product granular model with uncertain image data provided by domain experts. This image data is generated by a process of vague image tagging where experts label regions in the image using vague and general shapes. This is possible through a number of observations of, and assumptions about, human behaviour and the human visual system. We focus on the human tendency to concentrate on one central region of interest at a time and from this characteristic we define an applicability function across each tagged shape. We present bio-mimetic justification for our choice of applicability function and show examples of the vague tagging process and machine learning with this tagged data using a cross-product granule learner. Illustrated applications include medical decision making from radiological images and guided training of robots in hazardous environments.

[1]  Jonathan Rossiter.  Humanist Computing: Modelling with Words, Concepts, and Behaviours , 2003, Modelling with Words.

[2]  Jing Hua,et al.  Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  S. Ullman.  High-Level Vision: Object Recognition and Visual Cognition , 1996 .

[4]  Jf Baldwin,et al.  Efficient algorithms for semantic unification , 1996 .

[5]  Jonathan Rossiter.  The rapid elicitation of knowledge about images using fuzzy information granules , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[6]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Carol Friedman,et al.  Research Paper: A General Natural-language Text Processor for Clinical Radiology , 1994, J. Am. Medical Informatics Assoc..

[8]  B. S. Manjunath,et al.  Image segmentation using curve evolution and flow fields , 2002, Proceedings. International Conference on Image Processing.

[9]  Sj Hogan,et al.  Computer-assisted diagnosis of CT pulmonary images , 2001 .

[10]  James Ze Wang,et al.  Unsupervised Multiresolution Segmentation for Images with Low Depth of Field , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  R. Manmatha,et al.  Automatic image annotation and retrieval using cross-media relevance models , 2003, SIGIR.

[12]  H L Kundel,et al.  Mechanism of satisfaction of search: eye position recordings in the reading of chest radiographs. , 1995, Radiology.

[13]  Jonathan Lawry,et al.  Modelling with words : learning, fusion, and reasoning within a formal linguistic representation framework , 2003 .

[14]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[15]  James G. Shanahan.  Soft computing for knowledge discovery introducing Cartesian granule features , 2000, The Kluwer international series in engineering and computer science.

[16]  B. S. Manjunath,et al.  Unsupervised Segmentation of Color-Texture Regions in Images and Video , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Trevor P Martin,et al.  Modelling with words using Cartesian granule features , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[18]  R. Manmatha,et al.  Using Maximum Entropy for Automatic Image Annotation , 2004, CIVR.

[19]  Alan C. Bovik,et al.  Analysis of multichannel narrow-band filters for image texture segmentation , 1991, IEEE Trans. Signal Process..

[20]  R. Manmatha,et al.  Automatic Image Annotation and Retrieval using CrossMedia Relevance Models , 2003 .

[21]  Jonathan Rossiter,et al.  Learning from uncertain image data using granular fuzzy sets and bio-mimetic applicability functions , 2005 .