Semantic based image retrieval: a probabilistic approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Content-Based Image Retrieval at the End of the Early Years
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
A Model-Based Approach to Semantic-Based Retrieval of Visual Information
SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
Towards Retrieval of Visual Information Based on the Semantic Models
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Image retrieval using color histograms generated by Gauss mixture vector quantization
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Sunset scene classification using simulated image recomposition
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
Fotofiti: web service for photo management
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
A scalable service for photo annotation, sharing, and search
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Review: Which is the best way to organize/classify images by content?
Image and Vision Computing
CueFlik: interactive concept learning in image search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Event recognition: viewing the world with a third eye
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Concept learning by fuzzy k-NN classification and relevance feedback for efficient image retrieval
Expert Systems with Applications: An International Journal
Overview based example selection in end user interactive concept learning
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Scene Categorization by Introducing Contextual Information to the Visual Words
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Scene categorization via contextual visual words
Pattern Recognition
A Bayesian network-based framework for semantic image understanding
Pattern Recognition
Beyond pixels: Exploiting camera metadata for photo classification
Pattern Recognition
Natural scene classification using overcomplete ICA
Pattern Recognition
Indoor vs. outdoor scene classification in digital photographs
Pattern Recognition
Automatic image annotation for semantic image retrieval
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Bayesian fusion of camera metadata cues in semantic scene classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An approach of multi-level semantics abstraction
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Indoor scene recognition by a mobile robot through adaptive object detection
Robotics and Autonomous Systems
| Hi-index | 0.00 |
Grouping images into (semantically) meaningful categories using low-level visual features is a challenging and important problem in content-based image retrieval. Using binary Bayesian classifiers, we attempt to capture high-level concepts from low-level image features under the constraint that the test image does belong to one of the classes of interest. Specifically, we consider the hierarchical classification of vacation images; at the highest level, images are classified into indoor/outdoor classes, outdoor images are further classified into city/landscape classes, and finally, a subset of landscape images is classified into sunset, forest, and mountain classes. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a vector quantizer can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. On a database of 6,931 vacation photographs, our system achieved an accuracy of 90.5% for indoor vs. outdoor classification, 95.3% for city vs. landscape classification, 96.6% for sunset vs. forest & mountain classification, and 95.5% for forest vs. mountain classification. We further develop a learning paradigm to incrementally train the classifiers as additional training samples become available and also show preliminary results for feature size reduction using clustering techniques.