Affine-Constrained Group Sparse Coding and Its Application to Image-Based Classification

Yu-Tseh Chi, Mohsen Ali, Jeffrey Ho

Abstract:

This paper proposes a novel approach for sparse coding
that further improves upon the sparse representation-based
classification (SRC) framework. The proposed framework,

Affine-Constrained Group Sparse Coding (ACGSC), ex-
tends the current SRC framework to classification problems

with multiple input samples. Geometrically, the affine-
constrained group sparse coding essentially searches for

the vector in the convex hull spanned by the input vectors
that can best be sparse coded using the given dictionary.

The resulting objective function is still convex and can be ef-
ficiently optimized using iterative block-coordinate descent

scheme that is guaranteed to converge. Furthermore, we
provide a form of sparse recovery result that guarantees,
at least theoretically, that the classification performance
of the constrained group sparse coding should be at least
as good as the group sparse coding. We have evaluated

the proposed approach using three different recognition ex-
periments that involve illumination variation of faces and

textures, and face recognition under occlusions. Prelimi-
nary experiments have demonstrated the effectiveness of the

proposed approach, and in particular, the results from the
recognition/occlusion experiment are surprisingly accurate
and robust.