Learning multi-label scene classification

✍ Scribed by Matthew R. Boutell; Jiebo Luo; Xipeng Shen; Christopher M. Brown

• Publisher: Elsevier Science
• Year: 2004
• Tongue: English
• Weight: 730 KB
• Volume: 37
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/j.patcog.2004.03.009

✦ Synopsis

In classic pattern recognition problems, classes are mutually exclusive by deÿnition. Classiÿcation errors occur when the classes overlap in the feature space. We examine a di erent situation, occurring when the classes are, by deÿnition, not mutually exclusive. Such problems arise in semantic scene and document classiÿcation and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classiÿcation, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a ÿeld scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a di erent treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classiÿcation; furthermore, our work appears to generalize to other classiÿcation problems of the same nature.