Paper Info
Title
Scene classification based on semantic labeling.
Abstract
Finding an appropriate image representation is a crucial problem in robotics. This problem has been classically addressed by means of computer vision techniques, where local and global features are used. The selection or/and combination of different features is carried out by taking into account repeatability and distinctiveness, but also the specific problem to solve. In this article, we propose the generation of image descriptors from general purpose semantic annotations. This approach has been evaluated as source of information for a scene classifier, and specifically using Clarifai as the semantic annotation tool. The experimentation has been carried out using the ViDRILO toolbox as benchmark, which includes a comparison of state-of-the-art global features and tools to make comparisons among them. According to the experimental results, the proposed descriptor performs similarly to well-known domain-specific image descriptors based on global features in a scene classification task. Moreover, the proposed descriptor is based on generalist annotations without any type of problem-oriented parameter tuning.
Year
DOI
Venue
2016
10.1080/01691864.2016.1164621
ADVANCED ROBOTICS
Keywords
Field
DocType
Scene classification,semantic labeling,machine learning,data engineering
General purpose,Computer science,Image representation,Toolbox,Semantic labeling,Information engineering,Artificial intelligence,Classifier (linguistics),Robotics,Machine learning,Optimal distinctiveness theory
Journal
Volume
Issue
ISSN
30
11-12
0169-1864
Citations 
PageRank 
References 
8
0.47
11
Authors
6
Name
Order
Citations
PageRank
José Carlos Rangel1243.11
Miguel Cazorla232544.17
Ismael García-varea327536.16
Jesus Martínez-Gómez411511.09
Élisa Fromont519225.51
Marc Sebban690661.18