Adaptive Texture Recognition in Image Sequences with Prediction through Features Interpolation

Abstract

This paper presents a prediction method for an efficient on-line model modification in adaptive texture image recognition issue. The approach builds a close-loop interaction between object recognition and model modification systems. Object recognition applies an advanced RBF classifier in order to recognize objects on a current image of a sequence. Model modification manipulates the RBF classifier models by changing the structure of the classifier or/and parameters of classifier nodes in order to adapt to changing situations. For efficient model modification, this work includes modification through prediction that classifier models are changed in advance for adaptation if it is possible to predict the modification patterns. For experimentation, the change of texture characteristics has been investigated over a sequence of texture images acquired under dynamic perceptual conditions. Texture characteristics on images in a sequence have been extracted by Gabor spectral filtering, Laws’ energy filtering and Wavelet Transformation filtering. The results of the investigation justify the need for an on-line model modification over the entire sequence of images in order to preserve the system recognition capability, and present the possibility of prediction by finding partial patterns of texture characteristics change. According to experimental results, it is seen that the prediction in model modification can enhance the competence of the system through experimentations.