Query-sensitive embeddings

Athitsos, Vassilis; Hadjieleftheriou, Marios; Kollios, George; Sclaroff, Stan

doi:10.1145/1242524.1242525

Cited by 19 publications

(37 citation statements)

References 34 publications

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“…A similarity matrix for S 1 and S 2 is an |E 1 | × |E 2 | matrix att of numbers in the range [0, 1]. For any A ∈ E 1 and B ∈ E 2 , att(A, B) indicates the suitability of mapping A to B, as determined by human domain experts or computed by an existing algorithm, e.g., [5,13,21].…”

Section: Schema Level Embeddingsmentioning

confidence: 99%

Information preserving XML schema embedding

Fan

Bohannon

2008

ACM Trans. Database Syst.

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A fundamental concern of information integration in an XML context is the ability to embed one or more source documents in a target document so that (a) the target document conforms to a target schema and (b) the information in the source document(s) is preserved. In this paper, information preservation for XML is formally studied, and the results of this study guide the definition of a novel notion of schema embedding between two XML DTD schemas represented as graphs. Schema embedding generalizes the conventional notion of graph similarity by allowing an edge in a source DTD schema to be mapped to a path in the target DTD. Instance-level embeddings can be defined from the schema embedding in a straightforward manner, such that conformance to a target schema and information preservation are guaranteed. We show that it is NP-complete to find an embedding between two DTD schemas. We also provide efficient heuristic algorithms to find candidate embeddings, along with experimental results to evaluate and compare the algorithms. These yield the first systematic and effective approach to finding information preserving XML mappings.

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Section: Schema Level Embeddingsmentioning

confidence: 99%

Information preserving XML schema embedding

Fan

Bohannon

2008

ACM Trans. Database Syst.

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“…where, as the data is discrete, the value α i x is cast to an integer in the range [1,256]. Our estimate of α i consists of two phases: firstly, for each x in [1,256] we compute the point y in [1,256] …”

Section: Diagonal Transform Computationmentioning

confidence: 99%

“…Our estimate of α i consists of two phases: firstly, for each x in [1,256] we compute the point y in [1,256] …”

Section: Diagonal Transform Computationmentioning

confidence: 99%

“…Note that, since the values of R, G, B are in [1,256], the values of α 0 R 0 , α 1 G 0 , α 2 B 0 possibly greater than 256 are truncated to 256 (saturated pixels). Therefore, to make the estimate robust (as much as possible) with respect to pixel saturation, the N th bin of the histograms H i 0 and H i 1 are not considered when determining the von Kries transform.…”

Section: Diagonal Transform Computationmentioning

confidence: 99%

“…Due to the dependency of the divergence (4) on the values α i (i = 0, 1, 2), δ does not satisfy the triangular inequality, and thus it is not a distance. Nevertheless, it is a query-sensitive dissimilarity measure, in the sense that it depends on the query [1]. The use of the score (6) instead of a L p metric (p ≥ 1) between the color histograms is justified by the major robustness of (6) to color quantization.…”

Section: Application To Image Recognitionmentioning

confidence: 99%

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Illuminant Change Estimation via Minimization of Color Histogram Divergence

Lecca

Messelodi

2009

Lecture Notes in Computer Science

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Abstract. We present a new method for computing the change of light possibly occurring between two pictures of the same scene. We approximate the illuminant variation with the von Kries diagonal transform and estimate it by minimizing a functional that measures the divergence between the image color histograms. Our approach shows good performances in terms of accuracy of the illuminant change estimation and of robustness to pixel saturation and Gaussian noise. Moreover we illustrate how the method can be applied to solve the problem of illuminant invariant image recognition. Light and ColorColor descriptors are considered among the most important features in contentbased image retrieval and indexing [15]. Colors are in fact robust to noise, rescaling, rotation and image resolution. The main drawback in the use of color for object and image retrieval is the strict dependency of the color on the light in the scene. Color variations can be produced in different ways, for instance by changing the number, the position or the spectrum of the light sources. Moreover, the color of a picture often depends on the characteristics of the device used to capture the scene. The development of a device-and illuminant-invariant image representation is an old but still unsolved attractive problem in Computer Vision [15]. In this paper, we propose a method for estimating the variation of illuminant between the images of a scene taken under different light conditions. More precisely, we restrict our attention to the photometric changes induced by different kinds of lamps or by variations in the voltage of the lamps illuminating a scene. We assume that the illumination varies uniformly over the whole image and we assume the von Kries diagonal model, in which the responses of a camera sensor under two different illuminants are related by a diagonal linear transformation. This model has been proved to be a good approximation for illuminant changes [6], [7], especially in the case of narrow-band sensory systems [4], and it is employed in many color enhancement techniques, e.g. [2], [5], [3], [16]. Our technique estimates the von Kries transform between an image and a re-illuminated version of it by a least-squares method that minimizes a dissimilarity measure, named divergence, between their color histograms. The accuracy of the estimate obtained by our method has been measured on synthetic and real-world datasets,

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On the von Kries Model: Estimation, Dependence on Light and Device, and Applications

Lecca

2013

Lecture Notes in Computational Vision and Biomechanics

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Query-sensitive embeddings

Cited by 19 publications

References 34 publications

Information preserving XML schema embedding

Information preserving XML schema embedding

Illuminant Change Estimation via Minimization of Color Histogram Divergence

On the von Kries Model: Estimation, Dependence on Light and Device, and Applications

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