An Analytic Distance Metric for Gaussian Mixture Models with Application in Image Retrieval
In this paper we propose a new distance metric for probability density functions (PDF). The main advantage of this metric is that unlike the popular Kullback-Liebler (KL) divergence it can be computed in closed form when the PDFs are modeled as Gaussian Mixtures (GM). The application in mind for this metric is histogram based image retrieval. We experimentally show that in an image retrieval scenario the proposed metric provides as good results as the KL divergence at a fraction of the computational cost. This metric is also compared to a Bhattacharyya-based distance metric that can be computed in closed form for GMs and is found to produce better results.
KeywordsProbability Density Function Probability Density Function Gaussian Mixture Model Image Retrieval Color Histogram
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- 2.Han, J., Ma, K.: Fuzzy Color Histogram and its use in Color Image Retrieval. IEEE Trans. on Image Processing 11(8) (August 2003)Google Scholar
- 3.Del Bimbo, A.: Visual information Retrieval. Morgan Kaufmann publishers, San Francisco (1999)Google Scholar
- 5.Ray, S.: Distance-Based Model Selection with Application to the Analysis of Gene Expression Data. Ms Thesis, Dept. of Statistics, Pennsylvania State University (2003)Google Scholar