GridShift: A Faster Mode-seeking Algorithm for Image Segmentation and Object Tracking
This work addresses the speed bottleneck in mode-seeking algorithms for computer vision tasks like image segmentation and object tracking, offering a practical improvement for researchers and practitioners in these fields.
The paper tackles the computational inefficiency of mean shift for large datasets by proposing GridShift, a faster mode-seeking algorithm that uses a grid-based neighbor search and moves active grid cells instead of data points, achieving linear runtime in active grid cells and demonstrating superior accuracy and runtime on benchmark image segmentation datasets.
In machine learning and computer vision, mean shift (MS) qualifies as one of the most popular mode-seeking algorithms used for clustering and image segmentation. It iteratively moves each data point to the weighted mean of its neighborhood data points. The computational cost required to find the neighbors of each data point is quadratic to the number of data points. Consequently, the vanilla MS appears to be very slow for large-scale datasets. To address this issue, we propose a mode-seeking algorithm called GridShift, with significant speedup and principally based on MS. To accelerate, GridShift employs a grid-based approach for neighbor search, which is linear in the number of data points. In addition, GridShift moves the active grid cells (grid cells associated with at least one data point) in place of data points towards the higher density, a step that provides more speedup. The runtime of GridShift is linear in the number of active grid cells and exponential in the number of features. Therefore, it is ideal for large-scale low-dimensional applications such as object tracking and image segmentation. Through extensive experiments, we showcase the superior performance of GridShift compared to other MS-based as well as state-of-the-art algorithms in terms of accuracy and runtime on benchmark datasets for image segmentation. Finally, we provide a new object-tracking algorithm based on GridShift and show promising results for object tracking compared to CamShift and meanshift++.