Accelerated Sub-Image Search For Variable-Size Patches Identification Based On Virtual Time Series Transformation And Segmentation
This addresses efficiency challenges in agricultural or aerial image analysis for tasks like object detection and patch identification, but it is incremental as it builds on existing search methods with a new acceleration mechanism.
The paper tackles the problem of identifying fixed-size objects and variable-size patches in aerial images by accelerating sub-image search, achieving solve time reductions of up to 100 times compared to exhaustive search.
This paper addresses two tasks: (i) fixed-size objects such as hay bales are to be identified in an aerial image for a given reference image of the object, and (ii) variable-size patches such as areas on fields requiring spot spraying or other handling are to be identified in an image for a given small-scale reference image. Both tasks are related. The second differs in that identified sub-images similar to the reference image are further clustered before patches contours are determined by solving a traveling salesman problem. Both tasks are complex in that the exact number of similar sub-images is not known a priori. The main discussion of this paper is presentation of an acceleration mechanism for sub-image search that is based on a transformation of an image to multivariate time series along the RGB-channels and subsequent segmentation to reduce the 2D search space in the image. Two variations of the acceleration mechanism are compared to exhaustive search on diverse synthetic and real-world images. Quantitatively, proposed method results in solve time reductions of up to 2 orders of magnitude, while qualitatively delivering comparative visual results. Proposed method is neural network-free and does not use any image pre-processing.