Adaptive object recognition with image feature interpolation
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Aerial photo image retrieval using adaptive image classification
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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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.