Mixture of Subspaces Image Representation and Compact Coding for Large-Scale Image Retrieval.

There are two major approaches to content-based image retrieval using local image descriptors. One is descriptor-by-descriptor matching and the other is based on comparison of global image representation that describes the set of local descriptors of each image. In large-scale problems, the latter is preferred due to its smaller memory requirements; however, it tends to be inferior to the former in terms of retrieval accuracy. To achieve both low memory cost and high accuracy, we investigate an asymmetric approach in which the probability distribution of local descriptors is modeled for each individual database image while the local descriptors of a query are used as is. We adopt a mixture model of probabilistic principal component analysis. The model parameters constitute a global image representation to be stored in database. Then the likelihood function is employed to compute a matching score between each database image and a query. We also propose an algorithm to encode our image representation into more compact codes. Experimental results demonstrate that our method can represent each database image in less than several hundred bytes achieving higher retrieval accuracy than the state-of-the-art method using Fisher vectors.