EVA: an experimental video annotator for symbolic analysis of video data
ACM SIGCHI Bulletin
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Efficient use of local edge histogram descriptor
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Ontology-Based Photo Annotation
IEEE Intelligent Systems
OVID: Design and Implementation of a Video-Object Database System
IEEE Transactions on Knowledge and Data Engineering
Region-based image retrieval using an object ontology and relevance feedback
EURASIP Journal on Applied Signal Processing
Fusing MPEG-7 visual descriptors for image classification
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Efficient energies and algorithms for parametric snakes
IEEE Transactions on Image Processing
High-level concept annotation using ontology and probabilistic inference
Proceedings of the First International Conference on Internet Multimedia Computing and Service
A review on automatic image annotation techniques
Pattern Recognition
SIA: semantic image annotation using ontologies and image content analysis
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
DOGI: an annotation system for images of dog breeds
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Multimedia Tools and Applications
| Hi-index | 0.00 |
One of the big issues facing current content-based image retrieval is how to automatically extract the high-level concepts from images. In this paper, we present an efficient system that automatically extracts the high-level concepts from images by using ontologies and semantic inference rules. In our method, MPEG-7 visual descriptors are used to extract the visual features of image, and the visual features are mapped to semi-concepts via the mapping algorithm. We also build the visual and animal ontologies to bridge the semantic gap. The visual ontology allows the definition of relationships among the classes describing the visual features and has the values of semi-concepts as the property values. The animal ontology can be exploited to identify the highlevel concept in an image. Also, the semantic inference rules are applied to the ontologies to extract the high-level concept. Finally, we evaluate the proposed system using the image data set including various animal objects and discuss the limitations of our system.