Semantic road segmentation via multi-scale ensembles of learned features

Jose M. Alvarez, Yann LeCun, Theo Gevers, Antonio M. Lopez

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand-designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process. Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state-of-the-art methods using other sources of information such as depth, motion or stereo.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings
PublisherSpringer Verlag
Number of pages10
EditionPART 2
ISBN (Print)9783642338670
StatePublished - 2012
EventComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7584 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceComputer Vision, ECCV 2012 - Workshops and Demonstrations, Proceedings

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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