Unsupervised classemes

Authors:
Claudio Cusano;Riccardo Satta;Simone Santini
Affiliations:
Department of Informatics, Systems and Communication (DISCo), Università degli Studi di Milano-Bicocca, Milano, Italy;Department of Electrical and Electronic Engineering, Università di Cagliari, Italy;Escuela Politécnica Superior, Universidad Autónoma de Madrid, Spain
Venue:
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part III
Year:
2012

Citing 13
Cited 0

SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope

International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Automatic photo pop-up

ACM SIGGRAPH 2005 Papers
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Large-Scale Concept Ontology for Multimedia

IEEE MultiMedia
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval

International Journal of Computer Vision
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

International Journal of Computer Vision
CEDD: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval

ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Object Detection with Discriminatively Trained Part-Based Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient object category recognition using classemes

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Prosemantic features for content-based image retrieval

AMR'09 Proceedings of the 7th international conference on Adaptive multimedia retrieval: understanding media and adapting to the user
The MPEG-7 visual standard for content description-an overview

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a new model of semantic features that, unlike previously presented methods, does not rely on the presence of a labeled training data base, as the creation of the feature extraction function is done in an unsupervised manner. We test these features on an unsupervised classification (clustering) task, and show that they outperform primitive (low-level) features, and that have performance comparable to that of supervised semantic features, which are much more expensive to determine relying on the presence of a labeled training set to train the feature extraction function.