Low-level invariant image retrieval based on results fusion

Abstract

This paper presents a results fusion approach through multiple representations and multiple queries to tackle the problem of invariance in content-based image retrieval (CBIR). We consider the case of textures. This approach considers invariance at the query level rather than at the representation level. We use two models to capture the textural visual content of images: the autoregressive (AR) model and a perceptual (PCP) model based on a set of perceptual features. The perceptual model is used with two viewpoints: the original images viewpoint and the autocovariance function viewpoint. These representation models are not invariant with respect to geometric and photometric transformations. Then, by using results fusion of multiple representations and/or multiple queries, we show that retrieval effectiveness is improved in an important way even though the representation models are not invariant. The resulting invariant texture retrieval algorithm has two levels of results fusion (merging): 1. The first level consists in merging results returned by the different models / viewpoints (representations) for the same query in one results list using appropriate fusion models; 2. The second level consists in merging each fused list of results of different queries into a unique fused list using appropriate fusion models. Experimental retrieval results are promising.