Domain-Size Pooling in Local Descriptors: DSP-SIFT

Abstract

We introduce a simple modification of local image descriptors, such as SIFT, based on pooling gradient orientations across different domain sizes, in addition to spatial locations. The resulting descriptor, which we call DSP-SIFT, outperforms other methods in wide-baseline matching benchmarks, including those based on convolutional neural networks, despite having the same dimension of SIFT and requiring no training.

Extended version of the CVPR 2015 paper. Technical Report UCLA CSD 140022

References in corpus (3)

• Return of the Devil in the Details: Delving Deep into Convolutional Nets
• Descriptor Matching with Convolutional Neural Networks: a Comparison to SIFT
• Visual Scene Representations: Contrast, Scaling and Occlusion