φ<sup>3</sup>, φ<sup>4</sup> model with quenched impurities

Schiele, K.

doi:10.1002/pssb.2221340246

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A Basic Indexing Algorithm1

The Basic Bayesian Model1

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1997

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Cited by 1 publication

(2 citation statements)

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“…However, this is not necessarily the case. Following a suggestion made by Schiele [8], we observed the distribution of the invariants collected in our image data base, which contains over 150,000 9-dimensional vectors. Figure 4 represents the repartition of invariants 5 and 6, showing the ha- Fig.…”

Section: A Basic Indexing Algorithmmentioning

confidence: 99%

“…In a study on the distribution of the measure in some "receptive fields", Schiele [8] derived a Bayesian model which allowed the distribution of the measures to be taken into account. In this section we take a similar approach, deriving a model which uses the a priori knowledge of the distribution of the invariants in the matching process.…”

Section: The Basic Bayesian Modelmentioning

confidence: 99%

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Bayesian decision versus voting for image retrieval

Mohr

Picard

Schmid

1997

Computer Analysis of Images and Patterns

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Abstract. Image retrieval from image databases is usually performed by using global image characteristics. However the use of local image information is highly desirable when only part of the image is of interest. An original solution was introduced in [9] using invariant local signal characteristics. This paper extends this contribution by extending the set of invariants considered to allow illumination change. Then it is shown that the invariant distribution is far from uniform and a probabilistic indexing scheme is proposed. Experimental results validate the approch and the different methods are discussed.

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Section: A Basic Indexing Algorithmmentioning

confidence: 99%

Section: The Basic Bayesian Modelmentioning

confidence: 99%