Online model modification for adaptive texture recognition in image sequences

This paper presents and validates a method for adaptive texture recognition in image sequences under dynamic perceptual conditions and, consequently, under changing texture characteristics. The approach builds a closed-loop interaction between texture recognition and model modification systems. Texture recognition applies a modified radial-basis function (RBF) classifier to a current image of a sequence. The feedback reinforcement generation mechanism evaluates the classification results when compared to the previous images and activates classifier modification, if needed. Classifier modification selects a strategy and employs four behaviors in adapting the classifier´s structure and parameters. These behaviors include accommodation, translation, generation, and extinction applied to selected classifier components. Accommodation modifies the component´s boundary/spread. Translation shifts a given component over the feature space. Generation creates a new component of the RBF classifier. Extinction eliminates components that are no longer in use. The evolved RBF model is verified in order to confirm applied model modifications. Experimental results are presented for indoor and outdoor image sequences. The approach is validated and compared with traditional nonadaptive methods for texture recognition.