Object detection is an important task in computer vision. Recently, several unsupervised approaches have been proposed to cope with this problem in a category-independent manner. This work evaluates the adoption of a hierarchical graph-based segmentation along with an state-of-the-art method to detect object-related regions. A hierarchical segmentation approach produces a set of partitions at different detail levels, in a way that a coarser level can be obtained by a simple merge of finer ones. Experimental results show that our proposal obtains an increase of 11% in object detection rate.
CITATION STYLE
Ribeiro, R. M., Guimarães, S. J. F., & Patrocínio, Z. K. G. (2019). Hierarchical graph-based segmentation in detection of object-related regions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11401 LNCS, pp. 124–132). Springer Verlag. https://doi.org/10.1007/978-3-030-13469-3_15
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