Object recognition with adaptive Gabor features

摘要

We present a novel adaptive-sampling algorithm for spectral signature generation. Our algorithm is designed to increase inter-object discrimination and reduce feature-vector dimensionality. This algorithm is applied to a nonorthogonal-wavelet based multi-resolution object detection and recognition scheme. In this context we study and analyze the detection and identification of unknown objects in a complex background. Iterative optimization methods are employed to reduce computational demands during the learning phase. Our representation scheme takes into account all items in a given object library. It selects sample-point sets that maximize inter-object distance. Thus, the presented method increases identification robustness and can reduce the size of signature vectors.