Quad-tree segmentation for texture-based image query
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Text from WWW Images
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
NeTra: a toolbox for navigating large image databases
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Separation of Overlapping Text from Graphics
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Image similarity search with compact data structures
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Physics-motivated features for distinguishing photographic images and computer graphics
Proceedings of the 13th annual ACM international conference on Multimedia
A system for understanding imaged infographics and its applications
Proceedings of the 2007 ACM symposium on Document engineering
Slide image retrieval: a preliminary study
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
A semantic image classifier based on hierarchical fuzzy association rule mining
Multimedia Tools and Applications
| Hi-index | 0.00 |
We introduce NPIC, an image classification system that focuses on synthetic (e.g., non-photographic) images. We use class-specific keywords in an image search engine to create a noisily labeled training corpus of images for each class. NPIC then extracts both content-based image retrieval (CBIR) features and metadata-based textual features for each image for machine learning. We evaluate this approach on three different granularities: 1) natural vs. synthetic, 2) map vs. figure vs. icon vs. cartoon vs. artwork 3) and further subclasses of the map and figure classes. The NPIC framework achieves solid performance (99%, 97% and 85% in cross validation, respectively). We find that visual features provide a significant boost in performance, and that textual and visual features vary in usefulness at the different levels of granularities of classification.