CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines

By Edward Y. Chang, Kingshy Goh, Gerard Sychay, Gang Wu. IEEE Trans. Circuits Syst. Video Technol., 2003

501 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A review on automatic image annotation techniques (2012, result): “in the same way as text documents [17–40,115,116].”
Image retrieval: Ideas, influences, and trends of the new age (2008, unknown): “An approach to soft annotation, using Bayes Point machines, to give images a confidence level for each trained semantic label is explored in [Chang et al. 2003].”
Translating topics to words for image annotation (2007, methodology): “Existing techniques for automatic image annotation can be summarized into two categories [1, 6, 2].”
Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data (2013, background): “Text information is a common complement to image features in general [90], as well as medical CBIR research.”
Which is the best way to organize/classify images by content? (2007, background): “Also in [13] global features are used to produce a set of semantical labels with a certain belief for each image.”
SIFT and color feature fusion using localized maximum-margin learning for scene classification (2011, background): “In the early research work of scene categorization, global feature [2-4 , 8] based methods have been proposed.”
Content-based image retrieval: approaches and trends of the new age (2005, unknown): “annotation, using Bayes Point machines, to give images a confidence level for each trained semantic label has been explored in [14].”