An MLP-based texture segmentation method without selecting a feature set

A texture segmentation technique which employs a multilayer perceptron (MLP) and does not consider the selection of features is presented in this paper. Thus, users can avoid selection and computation of the feature set and hence real-time segmentation may be possible. The technique apparently works in a fashion similar to our visual system whereby we do not consciously compute any feature for texture discrimination. A detailed study has been made for the selection of the network size. A newly proposed variant of the back-propagation algorithm has been used for more efficient training of the network. An edge-preserving noise-smoothing approach has been proposed to remove noise from the segmented image.

[1]  Alexander A. Sawchuk,et al.  Supervised Textured Image Segmentation Using Feature Smoothing and Probabilistic Relaxation Techniques , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Patrick C. Chen,et al.  Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm☆ , 1979 .

[3]  Ujjwal Bhattacharya,et al.  On the rate of convergence of perceptron learning , 1995, Pattern Recognit. Lett..

[4]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[5]  Rama Chellappa,et al.  Unsupervised Texture Segmentation Using Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[7]  A A Sawchuk,et al.  Noise updating repeated Wiener filter and other adaptive noise smoothing filters using local image statistics. , 1986, Applied optics.

[8]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[9]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[10]  Changjing Shang,et al.  Principal features-based texture classification with neural networks , 1994, Pattern Recognit..

[11]  Nirupam Sarkar,et al.  Improved fractal geometry based texture segmentation technique , 1993 .

[12]  Mengyang Liao,et al.  Texture classification and segmentation using simultaneous autoregressive random model , 1992, [1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems.

[13]  C. H. Chen,et al.  Handbook of Pattern Recognition and Computer Vision , 1993 .

[14]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[15]  T. Y. Young,et al.  A multiresolution approach to texture segmentation using neural networks , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[16]  Luc Van Gool,et al.  Texture analysis Anno 1983 , 1985, Comput. Vis. Graph. Image Process..

[17]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

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

[19]  John F. Haddon,et al.  Neural networks for the texture classification of temporally consistent segmented regions of FLIR sequences , 1994, Proceedings of the 12th IAPR International Conference on Pattern Recognition, Vol. 3 - Conference C: Signal Processing (Cat. No.94CH3440-5).

[20]  M. Nagao,et al.  Edge preserving smoothing , 1979 .

[21]  Bidyut Baran Chaudhuri,et al.  Texture Segmentation Using Fractal Dimension , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Ujjwal Bhattacharya,et al.  Self-adaptive learning rates in backpropagation algorithm improve its function approximation performance , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[23]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[24]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..