Local Hybrid Coding for Image Classification

Sparse coding has received considerable research attentions due to its competitive performance for SPM-based image classification algorithms. In sparse coding, each low-level image descriptor (e.g., SIFT) is quantized into a sparse vector using an over-complete dictionary. Two typical schemes for achieving the code sparsity are imposing ℓ₁-sparsity penalty on the coding coefficients, or selecting a set of fc-nearest-neighbor bases from the dictionary for locality-aware encoding. In this paper, we discover that different coding schemes usually produce substantially inconsistent coefficients, each preferring either ℓ₁-sparsity or bases-locality. We therefore conjecture that different schemes should be explored simultaneously to further enhance the quantization quality. To this end, we propose a novel ensemble framework, Local Hybrid Coding (LHC), to formalize a unified optimization problem for different coding schemes. Specifically, we quantize each image descriptor using two disjoint sets of dictionaries, fcNN bases and non-fcNN bases, from which we efficiently compute a hybrid representation comprising of local coding and sparse coding, respectively. Extensive experiments on three benchmarks verify that LHC can remarkably outperform several state-of-the-art methods for image classification tasks, and bare comparable complexity to the most efficient coding methods.