Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields
This addresses a scalability bottleneck in computer vision tasks like object detection for researchers and practitioners, but it is incremental as it builds on existing nearest-neighbor methods.
The paper tackles the problem of inefficient nearest-neighbor template matching for high-resolution data by introducing vector quantization and filtering in nearest-neighbor fields, achieving state-of-the-art performance in low-resolution and outperforming previous methods at higher resolutions.
Template matching is a fundamental problem in computer vision with applications in fields including object detection, image registration, and object tracking. Current methods rely on nearest-neighbour (NN) matching, where the query feature space is converted to NN space by representing each query pixel with its NN in the template. NN-based methods have been shown to perform better in occlusions, appearance changes, and non-rigid transformations; however, they scale poorly with high-resolution data and high feature dimensions. We present an NN-based method which efficiently reduces the NN computations and introduces filtering in the NN fields (NNFs). A vector quantization step is introduced before the NN calculation to represent the template with $k$ features, and the filter response over the NNFs is used to compare the template and query distributions over the features. We show that state-of-the-art performance is achieved in low-resolution data, and our method outperforms previous methods at higher resolution.