Segmented Gray-Code Kernels for Fast Pattern Matching

The gray-code kernels (GCK) family, which has Walsh Hadamard transform on sliding windows as a member, is a family of kernels that can perform image analysis efficiently using a fast algorithm, such as the GCK algorithm. The GCK has been successfully used for pattern matching. In this paper, we propose that the G4-GCK algorithm is more efficient than the previous algorithm in computing GCK. The G4-GCK algorithm requires four additions per pixel for three basis vectors independent of transform size and dimension. Based on the G4-GCK algorithm, we then propose the segmented GCK. By segmenting input data into
Ls
parts, the SegGCK requires only four additions per pixel for 3
Ls
basis vectors. Experimental results show that the proposed algorithm can significantly accelerate the full-search equivalent pattern matching process and outperforms state-of-the-art methods.