Object Recognition Using Multiresolution Trees

This paper presents an object recognition method based on recursive neural networks (RNNs) and multiresolution trees (MRTs). MRTs are a novel hierarchical structure proposed to represent both the set of homogeneous regions in which images can be divided and the evolution of the segmentation process performed to determine such regions. Moreover, knowing the optimal number of regions that should be extracted from the images is not critical for the construction of MRTs, that are also invariant w.r.t. rotations and translations. A set of experiments was performed on a subset of the Caltech benchmark database, comparing the performances of the MRT and directed acyclic graph (DAG) representations. The results obtained by the proposed object detection technique are also very promising in comparison with other state-of-the-art approaches available in the literature.