Optimization for networks and object recognition

The present book explores two different application areas of combinatorial optimization, the work presented here, indeed, is two fold, since it deals with two distinct problems, one related to data transfer in networks and the other to object recognition. Caching is an essential technique to improve throughput and latency in a vast variety of applications. The core idea is to duplicate content in memories distributed across the network, which can then be exploited to deliver requested content with less congestion and delay. Automatic object recognition has become, over the last decades, a central topic in the artificial intelligence research, with a significant burt over the last new year with the advent of the deep learning paradigm; the objective of the work discussed in the Part 2 of this book is an attempt at improving the performance of a natural images classifier introducing in the loop knowledge coming from the real world, expressed in terms of probability of a set of spatial relations between the objects in the images.

Autorentext

I received a PhD degree in Mathematics and Computer Science from Università Federico II di Napoli in 2018. My research activity has been related to graph theory and information theory with application to machine learning, images processing and network optimization with particular focus on image segmentation, object recognition and index coding.