Journal
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
Volume 9, Issue 2, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2457450.2457454
Keywords
Algorithms; Image classification; object detection; multifeature fusion; semantic context modeling
Categories
Funding
- European Commission [FP7-287704 CUBRIK]
Ask authors/readers for more resources
This article introduces an image classification approach in which the semantic context of images and multiple low-level visual features are jointly exploited. The context consists of a set of semantic terms defining the classes to be associated to unclassified images. Initially, a multiobjective optimization technique is used to define a multifeature fusion model for each semantic class. Then, a Bayesian learning procedure is applied to derive a context model representing relationships among semantic classes. Finally, this context model is used to infer object classes within images. Selected results from a comprehensive experimental evaluation are reported to show the effectiveness of the proposed approaches.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available