Multi-stream Convolutional Networks for Indoor Scene Recognition

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Inception Institute of Artificial Intelligence
  • Linköping University
  • United Arab Emirates University

Abstract

Convolutional neural networks (CNNs) have recently achieved outstanding results for various vision tasks, including indoor scene understanding. The de facto practice employed by state-of-the-art indoor scene recognition approaches is to use RGB pixel values as input to CNN models that are trained on large amounts of labeled data (ImageNet or Places). Here, we investigate CNN architectures by augmenting RGB images with estimated depth and texture information, as multiple streams, for monocular indoor scene recognition. First, we exploit the recent advancements in the field of depth estimation from monocular images and use the estimated depth information to train a CNN model for learning deep depth features. Second, we train a CNN model to exploit the successful Local Binary Patterns (LBP) by using mapped coded images with explicit LBP encoding to capture texture information available in indoor scenes. We further investigate different fusion strategies to combine the learned deep depth and texture streams with the traditional RGB stream. Comprehensive experiments are performed on three indoor scene classification benchmarks: MIT-67, OCIS and SUN-397. The proposed multi-stream network significantly outperforms the standard RGB network by achieving an absolute gain of 9.3%, 4.7%, 7.3% on the MIT-67, OCIS and SUN-397 datasets respectively.

Details

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings
EditorsMario Vento, Gennaro Percannella
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Computer Analysis of Images and Patterns - Salerno, Italy
Duration: 3 Sep 20195 Sep 2019
Conference number: 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume11678 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Computer Analysis of Images and Patterns
Abbreviated titleCAIP
CountryItaly
CitySalerno
Period03/09/201905/09/2019

    Research areas

  • Depth features, Scene recognition, Texture features

ID: 37822089