Mixture of ELM based experts with trainable gating network

Mixture of experts method is a neural network based ensemble learning that has great ability to improve the overall classification accuracy. This method is based on the divide and conquer principle, in which the problem space is divided between several experts by supervisition of gating network. In this paper, we propose an ensemble learning method based on mixture of experts which is named mixture of ELM based experts with trainable gating network (MEETG) to improve the computing cost and to speed up the learning process of ME. The structure of ME consists of multi layer perceptrons (MLPs) as base experts and gating network, in which gradient-based learning algorithm is applied for training the MLPs which is an iterative and time consuming process. In order to overcome on these problems, we use the advantages of extreme learning machine (ELM) for designing the structure of ME. ELM as a learning algorithm for single hidden-layer feed forward neural networks provides much faster learning process and better generalization ability in comparision with some other traditional learning algorithms. Also, in the proposed method a trainable gating network is applied to aggregate the outputs of the experts dynamically according to the input sample. Our experimental results and statistical analysis on 11 benchmark datasets confirm that MEETG has an acceptable performance in classification problems. Furthermore, our experimental results show that the proposed approach outperforms the original ELM on prediction stability and classification accuracy.

Texture image analysis and texture classification methods - A review

Tactile texture refers to the tangible feel of a surface and visual texture refers to see the shape or contents of the image. In the image processing, the texture can be defined as a function of spatial variation of the brightness intensity of the pixels. Texture is the main term used to define objects or concepts of a given image. Texture analysis plays an important role in computer vision cases such as object recognition, surface defect detection, pattern recognition, medical image analysis, etc. Since now many approaches have been proposed to describe texture images accurately. Texture analysis methods usually are classified into four categories: statistical methods, structural, model-based and transform-based methods. This paper discusses the various methods used for texture or analysis in details. New researches shows the power of combinational methods for texture analysis, which can't be in specific category. This paper provides a review on well known combinational methods in a specific section with details. This paper counts advantages and disadvantages of well-known texture image descriptors in the result part. Main focus in all of the survived methods is on discrimination performance, computational complexity and resistance to challenges such as noise, rotation, etc. A brief review is also made on the common classifiers used for texture image classification. Also, a survey on texture image benchmark datasets is included.