Shape Recognition Using Vector Quantization

We present a framework to recognize objects in images based on their silhouettes. In previous work we developed translation and rotation invariant classification algorithms for textures based on Fourier transforms in the polar space followed by dimensionality reduction. Here we present a new approach to recognizing shapes by following a similar classification step with a "soft" retrieval algorithm where the search of a shape database is based on the VQ centroids found by the classification step. Experiments presented on the MPEG-7 CE-Shape 1 database show significant gains in retrieval accuracy over previous work. An interesting aspect of this recognition algorithm is that the first phase of classification seems to be a powerful tool for both texture and shape recognition.