Consensus Image Feature Extraction with Ordered Directionally Monotone Functions

In this work we propose to use ordered directionally monotone functions to build an image feature extractor. Some theoretical aspects about directional monotonicity are studied to achieve our goal and a construction method for an image application is presented. Our proposal is compared to well-known methods in the literature as the gravitational method, the fuzzy morphology or the Canny method, and shows to be competitive. In order to improve the method presented, we propose a consensus feature extractor using combinations of the different methods. To this end we use ordered weighted averaging aggregation functions and obtain a new feature extractor that surpasses the results obtained by state-of-the-art methods.

[1]  Humberto Bustince,et al.  Ordered Directionally Monotone Functions: Justification and Application , 2018, IEEE Transactions on Fuzzy Systems.

[2]  Tim Wilkin,et al.  Weakly Monotonic Averaging Functions , 2015, Int. J. Intell. Syst..

[3]  Humberto Bustince,et al.  A framework for edge detection based on relief functions , 2014, Inf. Sci..

[4]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[5]  Rafael Muñoz-Salinas,et al.  A novel method to look for the hysteresis thresholds for the Canny edge detector , 2011, Pattern Recognit..

[6]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[7]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Humberto Bustince,et al.  Directional monotonicity of fusion functions , 2015, Eur. J. Oper. Res..

[9]  Hidenori Itoh,et al.  Image Filtering, Edge Detection, and Edge Tracing Using Fuzzy Reasoning , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Francisco José Madrid-Cuevas,et al.  On candidates selection for hysteresis thresholds in edge detection , 2009, Pattern Recognit..

[11]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Gleb Beliakov,et al.  Aggregation Functions: A Guide for Practitioners , 2007, Studies in Fuzziness and Soft Computing.

[13]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Manuel González Hidalgo,et al.  On the Choice of the Pair Conjunction–Implication Into the Fuzzy Morphological Edge Detector , 2015, IEEE Transactions on Fuzzy Systems.

[15]  M. Forero-Vargas,et al.  Fuzzy Thresholding and Histogram Analysis , 2003 .

[16]  Humberto Bustince,et al.  A gravitational approach to edge detection based on triangular norms , 2010, Pattern Recognit..

[17]  Allan D. Jepson,et al.  Benchmarking Image Segmentation Algorithms , 2009, International Journal of Computer Vision.

[18]  R. Yager.  Quantifier guided aggregation using OWA operators , 1996, Int. J. Intell. Syst..

[19]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[20]  R. Mesiar,et al.  Aggregation operators: properties, classes and construction methods , 2002 .

[21]  James C. Bezdek,et al.  A geometric approach to edge detection , 1998, IEEE Trans. Fuzzy Syst..

[22]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.