Please use this identifier to cite or link to this item:
|Title:||NPIC: Hierarchical synthetic image classification using image search and generic features||Authors:||Wang, F.
|Issue Date:||2006||Citation:||Wang, F.,Kan, M.-Y. (2006). NPIC: Hierarchical synthetic image classification using image search and generic features. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4071 LNCS : 473-482. ScholarBank@NUS Repository.||Abstract:||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. © Springer-Verlag Berlin Heidelberg 2006.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/40890||ISBN:||3540360182||ISSN:||03029743|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 28, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.