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

    Authors

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

    Comments

    Authors: contact us about adding a copy of your work at [email protected]

    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

    Share

    COinS
     

    Accessibility Statement

    This item was created or digitized prior to April 24, 2027, or is a reproduction of legacy media created before that date. It is preserved in its original, unmodified state specifically for research, reference, or historical recordkeeping. In accordance with the ADA Title II Final Rule, the University Libraries provides accessible versions of archival materials upon request. To request an accommodation for this item, please submit an accessibility request form.