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Deep Learning in Mining of Visual Content

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-3-030-34376-7

ISBN: 978-3-030-34375-0

Publisher: Springer International Publishing - Springer Journals

Jan 23, 2020

1 - 9 of 9 Chapters

Available

Introduction

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 1 –3

[Visual content mining has a long history and has been a central problem in the field of Computer Vision. It consists in finding and correctly labelling objects in images or video sequences, recognition of static and dynamic scenes. It is necessary in a large set of research and application...
Available

Supervised Learning Problem Formulation

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 5 –11

[In machine learning we distinguish various approaches between two extreme ones: unsupervised and supervised learning. The task of unsupervised learning consists in grouping similar data points in the description space thus inducing a structure on it. Then the data model can be expressed in terms...
Available

Neural Networks from Scratch

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 13 –20

[Artificial neural networks consist of distributed information processing units. In this chapter, we define the components of such networks. We will first introduce the elementary unit: the formal neuron proposed by McCulloch and Pitts. Further we will explain how such units can be assembled to...
Available

Optimization Methods

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 21 –33

[The machine learning models aim to construct a prediction function which minimizes the loss function. There are many algorithms which aim to minimize the loss function. Most of them are iterative and operate by decreasing the loss function following a descent direction. These methods solve the...
Available

Deep in the Wild

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 35 –48

[In this chapter we are interested in how from high-resolution images and videos passing them through a Deep convolutional neural network we get reduced dimension which finally allows a classification decision. We are interested in two operations: convolution and pooling and trace analogy with...
Available

Convolutional Neural Networks as Image Analysis Tool

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 49 –58

[After studies of fundamental operations of convolution and sub-sampling in previous chapter, we introduce here convolutional neural networks and consider those designed for particular data: images. First of all we will expose some general principles, then go into detail layer-by-layer and...
Available

Dynamic Content Mining

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 59 –69

[Neural networks and convolutional neural networks can be considered as functions which take as input a vector and compute a distribution over the set of possible classes. Such networks have no notion of order in time nor in memory. That is they are not suitable for dynamic content mining like...
Available

Case Study for Digital Cultural Content Mining

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 71 –85

[In this chapter we consider an application case of Deep Learning in the task of architectural recognition. The main objective is to identify both: architectural styles and specific architectural structures. We are interested in attention mechanisms in Deep CNNs and explain how real visual...
Available

Introducing Domain Knowledge

Zemmari, Akka; Benois-Pineau, Jenny

Pages: 87 –97

[In this chapter we will consider another application case of Deep learning: classification of brain images for detection of Alzheimer’s disease. In this particular application of medical imaging domain, Deep NNs have become the mandatory tool. In this chapter we give some highlights on how the...

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