The effect multiple query representations on information retrieval system performance
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Handbook of pattern recognition & computer vision
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Fusion Via a Linear Combination of Scores
Information Retrieval
An application of multiple viewpoints to content-based image retrieval
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Autocovariance-based Perceptual Textural Features Corresponding to Human Visual Perception
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Improving image retrieval effectiveness via multiple queries
MMDB '03 Proceedings of the 1st ACM international workshop on Multimedia databases
Content representation and similarity matching for texture-based image retrieval
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Merging Results for Distributed Content Based Image Retrieval
Multimedia Tools and Applications
Information retrieval from visual databases using multiple representations and multiple queries
Proceedings of the 2009 ACM symposium on Applied Computing
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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.