Low-level invariant image retrieval based on results fusion

Abbadeni, Noureddine; Alhichri, Haikel

doi:10.1109/icme.2008.4607667

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…Invariant image retrieval is the ability to retrieve all relevant images to a query even if some of them have been transformed according to different geometric and photometric transformations such as rotation, scaling, illumination, viewpoint change and contrast change as well as non-rigid transformations (Zhang and Tan, 2002), (Lazebnik et al, 2004), (Abbadeni and Alhichri, 2008). We propose to use both multiple representations and multiple queries to address this difficult problem.…”

Section: Multiple Queriesmentioning

confidence: 99%

Texture Representation and Retrieval Based on Multiple Strategies

Abbadeni¹

2009

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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We propose an approach based on the fusion of multiple search strategies to content-based texture retrieval. Given the complexity of images and users' needs, there is no model or system which is the best than all the others in all cases and situations. Therefore, the basic idea of multiple search strategies is to use several models, several representations, several search strategies, several queries, etc. and then fuse (merge) the results returned by each model, representation, strategy or query in a unique list by using appropriate fusion models. Doing so, search effectiveness (relevance) should be improved without necessarily altering, in an important way, search efficiency. We consider the case of homogeneous textures. Texture is represented by three (3) models/viewpoints. We consider also the special case of invariance and use both multiple representations and multiple queries to address this difficult problem. Benchmarking carried out on two (2) image databases show that retrieval relevance (effectiveness) is improved in a very appreciable way with the fused model.

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Section: Multiple Queriesmentioning

confidence: 99%

Texture Representation and Retrieval Based on Multiple Strategies

Abbadeni¹

2009

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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“…In content-based image retrieval, among the rare works dealing with data fusion, we cite [10], [14], and [1]. In [10], a data fusion model working on distributed collections of images is proposed based on a normalization procedure of similarities among the various image collections.…”

Section: Introductionmentioning

confidence: 99%

“…Results merging coming from different channels is shown to improve performance in a very important way. In [1], a results fusion approach based on multiple queries was used to tackle the problem of invariant image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Texture Retrieval Effectiveness Improvement Using Multiple Representations Fusion

Abbadeni

2009

Advances in Image and Video Technology

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We propose a multiple representations approach to tackle the problem of content-based image retrieval effectiveness. Multiple representations is based on the use of multiple models or representations and make them cooperate to improve search effectiveness. We consider the case of homogeneous textures. Texture is represented using two different models: the well-known autoregressive model and a perceptual model based on perceptual features such as coarseness and directionality. In the case of the perceptual model, two viewpoints are considered: perceptual features are computed on original images and on the autocovariance function corresponding to original images. Thus, we use a total of three representations (models and viewpoints) to represent texture content. Simple results fusion models are used to merge search results returned by each of the three representations. Benchmarking carried out on the well-known Brodatz database using the recall graph is presented. Retrieval relevance (effectiveness) is improved in a very appreciable way with the fused model.

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“…The formula is a general expression of the similarity measure and can be used in other cases where images or objects cannot be always compared on all features. 2 …”

Section: %Hqfkpdunlqj Gdwdedvhmentioning

confidence: 99%

“…2 This database contains 22 classes of 40 640x480 images each class for a total of 880 images. 3 Images within the same classes have been taken with different photometric and geometric conditions.…”

Section: %Hqfkpdunlqj Gdwdedvhmentioning

confidence: 99%

Invariant Image Retrieval: An Approach Based On Multiple Representations and Multiple Queries

Abbadeni¹

2010

Multimedia

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We present an approach based on multiple representations and multiple queries to tackle the problem of invariance in the framework of content-based image retrieval. We consider the case of textures. This approach, rather than to consider invariance at the representation level, considers it at the query level. We use two models to represent the visual content of textures, 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 image's viewpoint and the autocovariance function viewpoint. After a brief presentation and discussion of these multiple representation models/viewpoints, we present the invariant texture retrieval algorithm. This algorithm considers results fusion (merging) at two different levels: 1. The first level consists in merging results returned by 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. Experimental retrieval results and benchmarking based on the precision/recall measures applied on a large image database show interesting results. ,QWURGXFWLRQContent-based image retrieval (CBIR) is a research area that has been active for more than a decade and many research results and prototypes have been carried out since then

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Low-level invariant image retrieval based on results fusion

Cited by 6 publications

References 13 publications

Texture Representation and Retrieval Based on Multiple Strategies

Texture Representation and Retrieval Based on Multiple Strategies

Texture Retrieval Effectiveness Improvement Using Multiple Representations Fusion

Invariant Image Retrieval: An Approach Based On Multiple Representations and Multiple Queries

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