CNN-aware binary MAP for general semantic segmentation

In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of...

Full description

Saved in:
Bibliographic Details
Main Authors: Ravanbakhsh, Mahdyar, Mousavi, Hossein, Nabi, Moin, Rastegari, Mohammad, Regazzoni, Carlo
Format: Conference Proceeding
Language:English
Subjects:
Binary codes
Convolutional codes
Convolutional Neural Networks
Encoding
Feature extraction
Image segmentation
Semantics
Visualization
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space. These binary codes are very robust against noise and non-semantic changes in the image. These binary encoding can be embedded into the CNN as an extra layer at the end of the network. This results in real-time segmentation. To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN. All the previous papers were limited to few number of category of the images (e.g. PASCAL VOC). Experiments show that our segmentation algorithm outperform the state-of-the-art non-semantic segmentation methods by large margin.
ISSN:2381-8549
DOI:10.1109/ICIP.2016.7532693