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In this paper, we present a multiple representations and multiple queries approach to tackle the problem of invariance in the framework of content-based image retrieval (CBIR), especially in the case of texture. This approach, rather than considering invariance at the representation level, considers it at the query level. We use two models to represent texture visual content, namely the autoregressive model and a perceptual 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. After a brief presentation and discussion of these multiple representation models / viewpoints, which are not invariant with respect to geometric and photometric transformations, we present the invariant texture retrieval algorithm, which is based on multiple models / viewpoints and multiple queries approach and consists in 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 a linear results fusion model; 2. The second level consists in merging each fused list of different queries into a unique fused list using a round robin fusion scheme. Experimentations show promising results. [ABSTRACT FROM AUTHOR]

In this paper we propose to revisit the well-known autoregressive model (AR) as a texture representation model. We consider the AR model with causal neighborhoods. First, we will define the AR model and discuss briefly the parameters estimation process. Then, we will present the synthesis algorithm and we will show some experimental results. The causal autoregressive model is applied in content-based image retrieval. Benchmarking conducted on the well-known Brodatz database shows interesting results. Both retrieval effectiveness (relevance) and retrieval efficiency are discussed and compared to the well-known multiresolution simultaneous autoregressive model (MRSAR). [ABSTRACT FROM AUTHOR]

This paper addresses the problem of texture retrieval by using a perceptual approach based on multiple viewpoints. We use a set of features that have a perceptual meaning corresponding to human visual perception. These features are estimated using a set of computational features that can be based on two viewpoints: the original images viewpoint and the autocovariance function viewpoint. The set of computational measures is applied to content-based image retrieval (CBIR) on a large image data set, the well-known Brodatz database, and is shown to give better results compared to related approaches. Furthermore, results fusion returned by each of the two viewpoints allows significant improvement in search effectiveness. [ABSTRACT FROM AUTHOR]