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Please use this identifier to cite or link to this item: http://hdl.handle.net/10277/866

Authors: Calefati, Alessandro
Internal Tutor: GALLO, IGNAZIO
Title: Discriminative feature learning for multimodal classification
Abstract: The purpose of this thesis is to tackle two related topics: multimodal classification and objective functions to improve the discriminative power of features. First, I worked on image and text classification tasks and performed many experiments to show the effectiveness of different approaches available in literature. Then, I introduced a novel methodology which can classify multimodal documents using singlemodal classifiers merging textual and visual information into images and a novel loss function to improve separability between samples of a dataset. Results show that exploiting multimodal data increases performances on classification tasks rather than using traditional single-modality methods. Moreover the introduced GIT loss function is able to enhance the discriminative power of features, lowering intra-class distance and raising inter-class distance between samples of a multiclass dataset.
Keywords: Multimodal clasification, dataset separability, deep features, machine learning, deep learning, image classification, text classification
Subject MIUR : INF/01 INFORMATICA
Issue Date: 2019
Language: eng
Doctoral course: Informatica e matematica del calcolo
Academic cycle: 32
Publisher: Università degli Studi dell'Insubria
Citation: Calefati, A.Discriminative feature learning for multimodal classification (Doctoral Thesis, Università degli Studi dell'Insubria, 2019).

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