New Algorithms for a Granular Image Recognition System

The paper describes new algorithms proposed for the granular pattern recognition system that retrieves an image from a collection of color digital pictures based on the knowledge contained in the object information granule (OIG). The algorithms use the granulation approach that employs fuzzy and rough granules. The information granules present knowledge concerning attributes of the object to be recognized. Different problems are considered depending on the full or partial knowledge where attributes are “color”, “location”, “size”, “shape”.

[1]  Xiaohui Liu,et al.  Automatic graph cut based segmentation of retinal optic disc by incorporating blood vessel compensation. , 2012, SOCO 2012.

[2]  Adam Krzyżak,et al.  A Novel Approach for Automatic Detection and Classification of Suspicious Lesions in Breast Ultrasound Images , 2013, J. Artif. Intell. Soft Comput. Res..

[3]  Joseph Lin Chu,et al.  The Recognition Of Partially Occluded Objects with Support Vector Machines, Convolutional Neural Networks and Deep Belief Networks , 2014, J. Artif. Intell. Soft Comput. Res..

[4]  Masaki Murata,et al.  Order Estimation of Japanese Paragraphs by Supervised Machine Learning and Various Textual Features , 2015, J. Artif. Intell. Soft Comput. Res..

[5]  Takashi Ogata,et al.  Experimental Development Of A Focalization Mechanism In An Integrated Narrative Generation System , 2015, J. Artif. Intell. Soft Comput. Res..

[6]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[7]  Z. Pawlak Granularity of knowledge, indiscernibility and rough sets , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[8]  Soumitra Dutta,et al.  Class-dependent rough-fuzzy granular space, dispersion index and classification , 2012, Pattern Recognit..

[9]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Danuta Rutkowska,et al.  Neuro-Fuzzy Architectures and Hybrid Learning , 2002, Studies in Fuzziness and Soft Computing.

[11]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[12]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[13]  Witold Pedrycz,et al.  The design of granular classifiers: A study in the synergy of interval calculus and fuzzy sets in pattern recognition , 2008, Pattern Recognit..

[14]  Elisabeth Rakus-Andersson,et al.  Information Granules in Application to Image Recognition , 2015, ICAISC.

[15]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[16]  Krzysztof Wiaderek,et al.  Fuzzy Granulation Approach to Color Digital Picture Recognition , 2013, ICAISC.

[17]  Elisabeth Rakus-Andersson,et al.  Fuzzy and Rough Techniques in Medical Diagnosis and Medication , 2007, Studies in Fuzziness and Soft Computing.

[18]  Ataollah Abbasi,et al.  Segmentation and Edge Detection Based on Modified ant Colony Optimization for Iris Image Processing , 2013, J. Artif. Intell. Soft Comput. Res..

[19]  Elisabeth Rakus-Andersson,et al.  Approximation and Rough Classification of Letter-Like Polygon Shapes , 2013, Rough Sets and Intelligent Systems.

[20]  George Vukovich,et al.  Granular Computing in Pattern Recognition , 2008 .

[21]  Andrzej Skowron,et al.  Rough Sets and Information Granulation , 2003, IFSA.

[22]  Marek R. Ogiela,et al.  Why Automatic Understanding? , 2007, ICANNGA.

[23]  Elisabeth Rakus-Andersson,et al.  Color Digital Picture Recognition Based on Fuzzy Granulation Approach , 2014, ICAISC.

[24]  Andrzej Skowron,et al.  Information granules: Towards foundations of granular computing , 2001 .

[25]  Thomas Fevens,et al.  Web–Based Framework For Breast Cancer Classification , 2014, J. Artif. Intell. Soft Comput. Res..

[26]  Brand Fortner,et al.  Number by Colors , 1997, Springer New York.

[27]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..