Multidimensional interactive fine-grained image retrieval

We propose an image retrieval methodology for a collection of similar images. By similar, we mean that one can define, for the collection, a set of dimensions, and for each of which a set of features. The dimensions are used to capture the essential characteristics of the images in the collection, and the features are for describing each image to a certain degree. We call this strategy fine-grained image retrieval to differentiate it from the more common coarse-grained retrieval, which does not assume any semantic properties on the image collection. The effectiveness of our methodology is demonstrated through an icon-based interactive retrieval system on a collection of butterfly images. This system provides the user with a friendly initial query-by-feature (QBF) interface. The user can then use query-by-example (QBE) to refine the query. In addition to presenting an outline of the methodology and the implementation on butterfly images, we also present some experimental results.