Probabilistic vs. geometric similarity measures for image retrieval

Similarity between images in image retrieval is measured by computing distances between feature vectors. This paper presents a probabilistic approach and describes two likelihood-based similarity measures for image retrieval. Popular distance measures like the Euclidean distance implicitly assign more more weighting to features with large ranges than those with small ranges. First, we discuss the effects of five feature normalization methods on retrieval performance. Then, we show that the probabilistic methods perform significantly better than geometric approaches like the nearest neighbor rule with city-block or Euclidean distances. They are also more robust to normalization effects and using better models for the features improves the retrieval results compared to making only general assumptions. Experiments on a database of approximately 10000 images show that studying the feature distributions are important and this information should be used in designing feature normalization methods and similarity measures