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Erschienen in:
2006 | OriginalPaper | Buchkapitel
NPIC: Hierarchical Synthetic Image Classification Using Image Search and Generic Features
verfasst von : Fei Wang, Min-Yen Kan
Erschienen in: Image and Video Retrieval
Verlag: Springer Berlin Heidelberg
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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.