Query expansion is a way to get the better search results from take the results of original query as new queries. And from the paper "Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval", it uses five different algorithm for query expansion, and evaluating the performance by the Oxford dataset.
For image search, feature extraction is a very important part. The latest few years a keypoint based feature presentation called "visual word" have been used and having better performance in object detection then other feature presentation.
To decide the "visual word", clustering method like "k-means" is the simplest way. But the training phase often needs long time, and we have observed that a hierarchical k-means(HKM) and locality sensitive hash(LSH) maybe could improve the efficient of training phase, and the two methods have some fundamental differences. So, we think that maybe we can use them in the query expansion to experiment that could one method can get better search result by using the search results by the combination of two result as new queries.
What we plan to do is the following:
1. training the visual word by HKM and LSH.
2. query image as a input, search for the visual word both method generated.
3. using the combination of both result lists for query expansion input for both method.
4. implementing the 5 query expansion method discussed in the paper and evaluating the performance.
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