A framework for moderate vocabulary semantic visual concept detection
Milind Ramesh Naphade, Ching‐Yung Lin, Apostol Natsev, Belle L. Tseng, J. R. Smith · 2003
Extraction of semantic features from visual concepts is essential for meaningful content management in terms of filtering, searching and retrieval. Recently, machine learning techniques have been shown to provide a computational framework to map low level features to high level semantics. In this paper we expose these techniques to the challenge of supporting a moderately large lexicon of semantic concepts. Using the TREC 2002 benchmark corpus for training and validation we investigate a support vector machine based learning system for modeling 34 visual concepts. The detection results show excellent performance for a set of concepts with moderately large training samples. Promising performance is also observed for concepts with few training concepts.