Indoor scene verification: evaluation of indoor scene representations for the purpose of location verification

Finfando, Filip (2020). Indoor scene verification: evaluation of indoor scene representations for the purpose of location verification. Thesis (Master thesis), E.T.S. de Ingenieros Informáticos (UPM).

Description

Title: Indoor scene verification: evaluation of indoor scene representations for the purpose of location verification
Author/s:
  • Finfando, Filip
Contributor/s:
  • Liu, Ying
  • Payberah, Amir
Item Type: Thesis (Master thesis)
Masters title: Data Science
Date: November 2020
Subjects:
Freetext Keywords: Computer vision; Perceptual similarity; Visual place recognition; Indoor scene localization; Deep neural networks
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (2MB) | Preview

Abstract

When human’s visual system is looking at two pictures taken in some indoor location, it is fairly easy to tell whether they were taken in exactly the same place, even when the location has never been visited in reality. It is possible due to being able to pay attention to the multiple factors such as spatial properties (windows shape, room shape), common patterns (floor, walls) or presence of specific objects (furniture, lighting). Changes in camera pose, illumination, furniture location or digital alteration of the image (e.g. watermarks) has little influence on this ability. Traditional approaches to measuring the perceptual similarity of images struggled to reproduce this skill. This thesis defines the Indoor scene verification (ISV) problem as distinguishing whether two indoor scene images were taken in the same indoor space or not. It explores the capabilities of state-of-the-art perceptual similarity metrics by introducing two new datasets designed specifically for this problem. Perceptual hashing, ORB, FaceNet and NetVLAD are evaluated as the baseline candidates. The results show that NetVLAD provides the best results on both datasets and therefore is chosen as the baseline for the experiments aiming to improve it. Three of them are carried out testing the impact of using the different training dataset, changing deep neural network architecture and introducing new loss function. Quantitative analysis of AUC score shows that switching from VGG16 to MobileNetV2 allows for improvement over the baseline.

More information

Item ID: 65862
DC Identifier: http://oa.upm.es/65862/
OAI Identifier: oai:oa.upm.es:65862
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 11 Jan 2021 11:55
Last Modified: 11 Jan 2021 11:55
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM