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https://scholarbank.nus.edu.sg/handle/10635/40890
Title: | NPIC: Hierarchical synthetic image classification using image search and generic features | Authors: | Wang, F. Kan, M.-Y. |
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 |
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