Shape Classification Using Hilbert Space Embeddings and Kernel Adaptive Filtering

Shape classification is employed for realizing image object identification and classification tasks. Most of the state-of-the-art approaches use sequential features extracted from contours to classify shapes, either directly, i.e., k-nearest neighbors (KNN), or through stochastic models, i.e., hidden Markov models (HMMs). Here, inspired by probability based metrics using Hilbert space embedding (HSE), we introduce a novel scheme for efficient shape classification. To this end, we highlight relevant curvature patterns from binary images towards a Kernel Adaptive Filtering (KAF)-based enhancement of the maximum mean discrepancy metric. Namely, we test the performance of our approach on the well-known MPEG-7 and 99-Shapes databases. Results show that our strategy can code relevant shape properties from binary images achieving competitive classification results.