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Abstract
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Article Information:
Engineering and Image Classification Framework Using Multi Instance Learning with KCCA Algorithm
P. Bhuvaneswari and S. Chitrakala
Corresponding Author: P. Bhuvaneswari
Submitted: March 18, 2012
Accepted: April 23, 2012
Published: December 15, 2012 |
Abstract:
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Image classification is a challenging task with many applications in computer vision. Images are
annotated with multiple keywords that may or may not correlated. Therefore, image classification may be
naturally modelled as Multiple Instance Learning problem. The main challenge of this problem is that usually
classes are overlapped and correlated. In single label classification the correlation among instance is not taken
into account. In an image the instance may belongs to several classes. The correlations among different tags
can significantly help predicting precise labels for improving the performance of multi label image
classification. This study proposes a method Kernel Canonical Correlation Analysis (KCCA) and Multi
Instance Learning for multi label image classification, for improving the performance of classification accuracy.
The proposed framework comprises an input image which can be partitioned into image patches and features
can be extracted. It breaks the original training set into several disjoint clusters of data. It then trains a multilabel
classifier from the data of each cluster. The K means clustering is used to perform automatic instance
cluster. Kernel canonical Correlation analysis can be made between disjoint clusters to know exact
correspondence between image patches. Multi Instance Learning is one potential solution to address the issue
of huge inter-concept visual similarity and improve the classification accuracy. The proposed approach reduces
the training time of standard multi-label classification algorithms, particularly in the case of large number of
labels.
Key words: Clustering, correlation analysis, multiple instance learning, normalized, , ,
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Cite this Reference:
P. Bhuvaneswari and S. Chitrakala, . Engineering and Image Classification Framework Using Multi Instance Learning with KCCA Algorithm. Research Journal of Applied Sciences, Engineering and Technology, (24): 5429-5433.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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