Perceptual meaning of the estimated parameters of the autoregressive model

2005

Multimedia Systems

In this paper, we show how the use of multiple content representations and their fusion can improve the performance of content-based image retrieval systems. We consider the case of texture and propose a new algorithm for texture retrieval based on multiple representations and their results fusion. Texture content is modeled 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 based on the original images viewpoint and on the autocovariance function viewpoint (corresponding to original images). So we consider a total of three content representations. The similarity measure used is based on Gower's index of similarity. Simple results of the fusion models are used to merge search results returned by different representations. Experimentations and benchmarking carried out on the well-known Brodatz database show a drastic improvement in search effectiveness with the fused model without necessarily altering their efficiency in an important way.

Section: Discussionmentioning

confidence: 99%

“…Retrieval experimental results presented later in this paper show that this version of the autoregressive model performs very well. In [1], we have proposed a perceptual interpretation of the estimated parameters of the autoregressive model as a measure of the degree of regularity/randomness of a texture.…”

Section: Discussionmentioning

confidence: 99%

Multiple representations, similarity matching, and results fusion for content-based image retrieval

2005

Multimedia Systems

“…Because of lack of space, we are not able to explain all the details related to these content representation models and to the similarity function. The reader can refer to [9] for more details about the perceptual model, to [5] for more details about the autoregressive model, and to [8] for more details about the similarity measure.…”

Section: Multiple Representationsmentioning

confidence: 99%

Information retrieval from visual databases using multiple representations and multiple queries

Proceedings of the 2009 ACM Symposium on Applied Computing

2009

This paper deals with information retrieval from visual databases. We propose an approach based on multiple representations, multiple queries, and the fusion of results returned by these different representations and queries. The basic idea of data fusion in information retrieval 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 of results by using appropriate fusion models. Doing so, retrieval effectiveness should be improved without necessarily altering, in an important way, retrieval efficiency. We consider both the general retrieval case as well as the invariant retrieval case. We consider the case of textures in particular. Benchmarking carried out on two (2) image databases shows that retrieval relevance is improved in a very appreciable way with the fused model.

“…The set of features of the perceptual model have a perceptual meaning by construction. The set of features derived from the autoregressive model have no perceptual meaning by construction, however we have proposed in [3] a perceptual interpretation of the set of features derived from the autoregressive model. This perceptual interpretation consists in considering those features as a measure of the randomness/regularity of the texture.…”

Section: Multiple Representationsmentioning

confidence: 99%

“…This perceptual interpretation consists in considering those features as a measure of the randomness/regularity of the texture. For more details on the perceptual model, refer to [7] and for more details on the autoregressive model, refer to [3], [2].…”

Section: Multiple Representationsmentioning

confidence: 99%

Texture Retrieval Effectiveness Improvement Using Multiple Representations Fusion

Advances in Image and Video Technology

2009

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.