An automatic feature generation approach to multiple instance learning and its applications to image databases

    Authors

    H. Cheng; K. A. Hua;N. Yu

    Comments

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    Abbreviated Journal Title

    Multimed. Tools Appl.

    Keywords

    Image categorization; Multiple-instance learning; Instance-level; clustering; Category pattern mining; Pattern feature generation; Bag-pattern distance; Multi-class classification; Computer Science, Information Systems; Computer Science, Software; Engineering; Computer Science, Theory & Methods; Engineering, Electrical; & Electronic

    Abstract

    Automatic content-based image categorization is a challenging research topic and has many practical applications. Images are usually represented as bags of feature vectors, and the categorization problem is studied in the Multiple-Instance Learning (MIL) framework. In this paper, we propose a novel learning technique which transforms the MIL problem into a standard supervised learning problem by defining a feature vector for each image bag. Specifically, the feature vectors of the image bags are grouped into clusters and each cluster is given a label. Using these labels, each instance of an image bag can be replaced by a corresponding label to obtain a bag of cluster labels. Data mining can then be employed to uncover common label patterns for each image category. These label patterns are converted into bags of feature vectors; and they are used to transform each image bag in the data set into a feature vector such that each vector element is the distance of the image bag to a distinct pattern bag. With this new image representation, standard supervised learning algorithms can be applied to classify the images into the pre-defined categories. Our experimental results demonstrate the superiority of the proposed technique in categorization accuracy as compared to state-of-the-art methods.

    Journal Title

    Multimedia Tools and Applications

    Volume

    47

    Issue/Number

    3

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    507

    Last Page

    524

    WOS Identifier

    WOS:000275800200008

    ISSN

    1380-7501

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