Object recognition using multiresolution trees

Authors:
Monica Bianchini;Marco Maggini;Lorenzo Sarti
Affiliations:
DII, Università degli Studi di Siena, Siena, Italy;DII, Università degli Studi di Siena, Siena, Italy;DII, Università degli Studi di Siena, Siena, Italy
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

Citing 7
Cited 3

Inductive Learning in Symbolic Domains Using Structure-Driven Recurrent Neural Networks

KI '96 Proceedings of the 20th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Monotonic Tree

DGCI '02 Proceedings of the 10th International Conference on Discrete Geometry for Computer Imagery
Similarity learning for graph-based image representations

Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Object Class Recognition Using Multiple Layer Boosting with Heterogeneous Features

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Recursive neural networks learn to localize faces

Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
2005 Special Issue: Recursive neural networks for processing graphs with labelled edges: theory and applications

Neural Networks - Special issue on neural networks and kernel methods for structured domains
A general framework for adaptive processing of data structures

IEEE Transactions on Neural Networks

Classification of graphical data made easy

Neurocomputing
A simple and effective neural model for the classification of structured patterns

KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Recognition of sequences of graphical patterns

ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.