Using residual vector quantization for image content classification

Multistage residual vector quantizers (RVQ) with optimal direct sum decoder codebooks have been successfully designed and implemented for data compression. Due to its multistage structure, RVQ has the ability to densely populate the input space with voronoi cell partitions. The same design concept has yielded good results in the application of image-content classification [1]. Furthermore, the multistage RVQ, with stage-wise codebooks, provides an opportunity to perform fine-grained feature attribution for image understanding, in general, and feature foundation data generation for natural and man-made structure recognition, in specific. In [1], the information at the stages of RVQ is heuristically integrated to perform class conditional pattern recognition; hence the process is not robust. Markov random field (MRF) provides a suitable Bayesian framework to integrate the information available at the various stages of RVQ to achieve optimized classification in the maximum a-posteriori s...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Syed Irteza Ali Khan, Christopher F. Barnes
• Keywords: ICASSP 2011, Multistage, Multistage Residual Vector, Optimal Direct Sum, Signal Processing