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Title of Journal: Multimed Tools Appl

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Abbravation: Multimedia Tools and Applications

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Springer US

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DOI

10.1016/0044-8486(91)90281-b

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1573-7721

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An automatic feature generation approach to multip

Authors: Hao Cheng Kien A Hua Ning Yu
Publish Date: 2009/07/29
Volume: 47, Issue: 3, Pages: 507-524
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Abstract

Automatic contentbased 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 MultipleInstance 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 predefined categories Our experimental results demonstrate the superiority of the proposed technique in categorization accuracy as compared to stateoftheart methodsAlgorithm 1 takes the iterative approach to reach a local minimum of the objective function defined in Eq 1 It starts with an initial guess of the vector Each run of Step 2 of the algorithm is to find the matched instances with regard to the current vector This guarantees to reduce the objective In Step 3 the vector to be computed is updated as the centroid of the matched instances which certainly decreases the objective value Therefore Algorithm 1 is sure to have the objective value smaller and smaller Because there are only a finite number of instances in mathcalYj c i there only exists a finite number of mapping and the objective function defined in Eq 1 is lowerbounded Overall Algorithm 1 guarantees to converge and the derived optimal vector gives a local minimum of the objective function

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