A Multi-class Kernel Alignment Method for Image Collection Summarization

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This paper proposes a method for involving domain knowledge in the construction of summaries of large collections of images. This is accomplished by using a multi-class kernel alignment strategy in order to learn a kernel function that incorporates domain knowledge (class labels). The kernel function is the basis of a clustering algorithm that generates a subset, the summary, of the image collection. The method was tested with a subset of the Corel image collection using a summarization quality measure based on information theory. Experimental results show that it is possible to improve the quality of the summary when domain knowledge is involved.