The Danger Theory Applied To Vegetal Image Pattern Classification

Artificial Immune Systems (AIS) are a type of intelligent algorithm inspired by the principles and processes of the human immune system. Despite the successful implementation of different AIS, the validity of the paradigm "self non self" have lifted many questions. The Danger theory was an alternative to this paradigm. If we involve its principles, the AIS are being applied as a classifier. However, image classification offers new prospects and challenges to data mining and knowledge extraction. It is an important tool and a descriptive task seeking to identify homogeneous groups of objects based on the values of their attributes. In this paper, we describe our initial framework in which the danger theory was apprehended by the Dendritic cells algorithm is applied to vegetal image classification. The approach classifies pixel in vegetal or soil class. Experimental results are very encouraging and show the feasibility and effectiveness of the proposed approach.

[1]  Alain Clément,et al.  Unsupervised segmentation of scenes containing vegetation (Forsythia) and soil by hierarchical analysis of bi-dimensional histograms , 2003, Pattern Recognit. Lett..

[2]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[3]  Uwe Aickelin,et al.  Danger Theory: The Link between AIS and IDS? , 2003, ICARIS.

[4]  Julie Greensmith,et al.  The DCA: SOMe comparison , 2008, Evol. Intell..

[5]  Jonathan Timmis,et al.  Artificial immune systems - a new computational intelligence paradigm , 2002 .

[6]  Jonathan Timmis,et al.  Application Areas of AIS: The Past, The Present and The Future , 2005, ICARIS.

[7]  Gu Ji-yan,et al.  The Dendritic Cell Algorithm , 2011 .

[8]  Julie Greensmith,et al.  Articulation and Clarification of the Dendritic Cell Algorithm , 2006, ICARIS.

[9]  Uwe Aickelin,et al.  The Danger Theory and Its Application to Artificial Immune Systems , 2008, ArXiv.

[10]  Julie Greensmith,et al.  Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection , 2005, ICARIS.

[11]  Julie Greensmith,et al.  Information fusion for anomaly detection with the dendritic cell algorithm , 2010, Inf. Fusion.

[12]  Richard Ford,et al.  Danger theory and collaborative filtering in MANETs , 2008, Journal in Computer Virology.

[13]  D. Dasgupta.  Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[14]  Julie Greensmith,et al.  The dendritic cell algorithm , 2007 .

[15]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[16]  Julie Greensmith,et al.  The Application of a Dendritic Cell Algorithm to a Robotic Classifier , 2007, ICARIS.

[17]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[18]  Julie Greensmith,et al.  The Deterministic Dendritic Cell Algorithm , 2008, ICARIS.

[19]  Leandro Nunes de Castro,et al.  Artificial Immune Systems: A New Computational Approach , 2002 .

[20]  Julie Greensmith,et al.  Dendritic Cells for Anomaly Detection , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[21]  Madhuri S. Mulekar.  Data Mining: Multimedia, Soft Computing, and Bioinformatics , 2004, Technometrics.

[22]  Sushmita Mitra,et al.  Data Mining , 2003 .

[23]  Julie Greensmith,et al.  Immune System Approaches to Intrusion Detection - A Review , 2004, ICARIS.