Automatic image annotation and retrieval using weighted feature selection |
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Authors: | Lei Wang Latifur Khan |
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Affiliation: | (1) Department of Computer Science, University of Texas at Dallas, Richardson, Texas 75083, USA |
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Abstract: | The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation
and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with
finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and
discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases.
However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these
dimensions, and become confounded in the process of dealing with these dimensions. In this paper, we propose weighted feature
selection algorithm as a solution to this problem. For a given cluster, we determine relevant features based on histogram
analysis and assign greater weight to relevant features as compared to less relevant features. We have implemented various
different models to link visual tokens with keywords based on the clustering results of K-means algorithm with weighted feature
selection and without feature selection, and evaluated performance using precision, recall and correspondence accuracy using
benchmark dataset. The results show that weighted feature selection is better than traditional ones for automatic image annotation
and retrieval. |
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Keywords: | Automatic image annotation Subspace clustering algorithm |
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