17 Result(s)
within
Chapter
Conference Paper
Luca Rossi
Image Processing and Computer Vision
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Chapter and Conference Paper
A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures
Spectral signatures have been used with great success in computer vision to characterise the local and global topology of 3D meshes. In this paper, we propose to use two widely used spectral signatures, the He...
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Chapter and Conference Paper
Classifying Me Softly: A Novel Graph Neural Network Based on Features Soft-Alignment
Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper we propose a new graph neural network architecture based on the soft-alignment of the gra...
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Chapter and Conference Paper
A Deep Learning-Based System for Product Recognition in Intelligent Retail Environment
This work proposes a pipeline that aims to recognize the products in a shelf, at the level of the single SKU (Stock Kee** Unit), starting from a photo of that shelf. It is composed of a first neural network ...
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Chapter and Conference Paper
People Counting on Low Cost Embedded Hardware During the SARS-CoV-2 Pandemic
Detecting and tracking people is a challenging task in a persistent crowded environment as retail, airport or station, for human behaviour analysis of security purposes. Especially during the global spread of ...
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Chapter and Conference Paper
The Average Mixing Kernel Signature
We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks. The average mix...
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Chapter and Conference Paper
GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild
The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in the scene. This problem remain...
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Chapter and Conference Paper
A Mixed Entropy Local-Global Reproducing Kernel for Attributed Graphs
In this paper, we develop a new mixed entropy local-global reproducing kernel for vertex attributed graphs based on depth-based representations that naturally reflect both local and global entropy based graph ...
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Chapter and Conference Paper
A Preliminary Survey of Analyzing Dynamic Time-Varying Financial Networks Using Graph Kernels
In this paper, we investigate whether graph kernels can be used as a means of analyzing time-varying financial market networks. Specifically, we aim to identify the significant financial incident that changes ...
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Chapter and Conference Paper
Measuring Vertex Centrality Using the Holevo Quantity
In recent years, the increasing availability of data describing the dynamics of real-world systems led to a surge of interest in the complex networks of interactions that emerge from such systems. Several meas...
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Chapter and Conference Paper
Adaptive Feature Selection Based on the Most Informative Graph-Based Features
In this paper, we propose a novel method to adaptively select the most informative and least redundant feature subset, which has strong discriminating power with respect to the target label. Unlike most tradit...
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Chapter and Conference Paper
A Nested Alignment Graph Kernel Through the Dynamic Time War** Framework
In this paper, we propose a novel nested alignment graph kernel drawing on depth-based complexity traces and the dynamic time war** framework. Specifically, for a pair of graphs, we commence by computing the...
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Chapter and Conference Paper
A Quantum Jensen-Shannon Graph Kernel Using Discrete-Time Quantum Walks
In this paper, we develop a new graph kernel by using the quantum Jensen-Shannon divergence and the discrete-time quantum walk. To this end, we commence by performing a discrete-time quantum walk to compute a ...
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Chapter and Conference Paper
An Edge-Based Matching Kernel Through Discrete-Time Quantum Walks
In this paper, we propose a new edge-based matching kernel for graphs by using discrete-time quantum walks. To this end, we commence by transforming a graph into a directed line graph. The reasons of using the...
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Chapter and Conference Paper
A Continuous-Time Quantum Walk Kernel for Unattributed Graphs
Kernel methods provide a way to apply a wide range of learning techniques to complex and structured data by shifting the representational problem from one of finding an embedding of the data to that of definin...
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Chapter and Conference Paper
A Quantum Jensen-Shannon Graph Kernel Using the Continuous-Time Quantum Walk
In this paper, we use the quantum Jensen-Shannon divergence as a means to establish the similarity between a pair of graphs and to develop a novel graph kernel. In quantum theory, the quantum Jensen-Shannon di...
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Chapter and Conference Paper
Attributed Graph Similarity from the Quantum Jensen-Shannon Divergence
One of the most fundamental problem that we face in the graph domain is that of establishing the similarity, or alternatively the distance, between graphs. In this paper, we address the problem of measuring th...
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Chapter and Conference Paper
Supervised Learning of Graph Structure
Graph-based representations have been used with considerable success in computer vision in the abstraction and recognition of object shape and scene structure. Despite this, the methodology available for learn...
