Abstract

GIST descriptor is a classic feature descriptor primarily used for scene categorization and recognition tasks. It drives a bank of Gabor filters, which respond to edges and textures at various scales and orientations to capture the spatial structures in an image. Compared to other scene recognition algorithms that rely on detailed object detection, GIST has lower computational complexity, allowing it to be widely applied. However, its internal multi-scale and multi-orientation Gabor filters also mean that systems based on it cannot be executed fast enough. This paper proposed an optimized GPU kernel for the GIST descriptor. It fully takes advantage of the symmetry of Gabor filters and proposes different optimization strategies for both oblique and orthogonal orientations. Extensive experiments demonstrate that the proposed kernel is adaptable to images of various scales and different GPUs. Compared to the cuFFT library, our kernel achieves 12.09x and 3.86x acceleration on an RTX 3080 GPU and a Jetson AGX Xavier GPU, respectively.