Title

An Automatic Feature Generation Approach To Multiple Instance Learning And Its Applications To Image Databases

Keywords

Bag-pattern distance; Category pattern mining; Image categorization; Instance-level clustering; Multi-class classification; Multiple-instance learning; Pattern feature generation

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. © 2009 Springer Science+Business Media, LLC.

Publication Date

5-1-2010

Publication Title

Multimedia Tools and Applications

Volume

47

Issue

3

Number of Pages

507-524

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/s11042-009-0335-3

Socpus ID

77950187584 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77950187584

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