Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logo Recognition by Recursive Neural Networks
GREC '97 Selected Papers from the Second International Workshop on Graphics Recognition, Algorithms and Systems
Towards Incremental Parsing of Natural Language Using Recursive Neural Networks
Applied Intelligence
Graph-based generation of referring expressions
Computational Linguistics
The Journal of Machine Learning Research
Neural Networks - Special issue on neural networks and kernel methods for structured domains
IEEE Transactions on Image Processing
Supervised neural networks for the classification of structures
IEEE Transactions on Neural Networks
A general framework for adaptive processing of data structures
IEEE Transactions on Neural Networks
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
The graph neural network model
IEEE Transactions on Neural Networks
Computational capabilities of graph neural networks
IEEE Transactions on Neural Networks
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Graph Neural Networks (GNNs) are a recently proposed connectionist model that extends previous neural methods to structured domains. GNNs can be applied on datasets that contain very general types of graphs and, under mild hypotheses, they have been proven to be universal approximators on graphical domains. Whereas most of the common approaches to graphs processing are based on a preliminary phase that maps each graph onto a simpler data type, like a vector or a sequence of reals, GNNs have the ability to directly process input graphs, thus embedding their connectivity into the processing scheme. In this paper, the main theoretical properties of GNNs are briefly reviewed and they are proposed as a tool for object localization. An experimentation has been carried out on the task of locating the face of a popular Walt Disney character in comic covers. In the dataset the character is shown in a number of different poses, often in cluttered backgrounds, and in high variety of colors. The proposed learning framework provides a way to deal with complex data arising from image segmentation process, without exploiting any prior knowledge on the dataset. The results are very encouraging, prove the viability of the method and the effectiveness of the structural representation of images.