Walter ten Brinke’s research while affiliated with Chris Clayton and other places

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Publications (2)


Fig. 2. A cow 
The Meaning of an Image in Content-Based Image Retrieval
  • Conference Paper
  • Full-text available

January 2006

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1,001 Reads

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7 Citations

Walter ten Brinke

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One of the major problems in CBIR is the so-called 'semantic gap': the dierence between low-level features, extracted from images, and the high-level 'information need' of the user. The goal of diminishing the semantic gap can be regarded as a quest for similar 'concepts' rather than similar features, where a concept is loosely defined as "what words (or images) stand for, signify, or mean" (1). We first seek to establish a metaphysical basis for CBIR. We look at ontological questions, such as 'what is similarity?' and 'what is an image?' in the context of CBIR. We will investigate these questions via thought experiments. We will argue that the meaning of an image—the concept it stands for—rests on at least three pillars: what actually can be seen on an image (its ontology), convention and imagination.

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Similarity: Measurement, Ordering and Betweenness

October 2004

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22 Reads

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6 Citations

Lecture Notes in Computer Science

This paper presents an overview of the challenges of producing a list of retrieval results ranked according to perceptual similarity. We explain some of the problems in using a metric to measure peceptual similarity, and consider the arguments for the desirability of metrics for retrieval. We discuss the use of broader definitions of betweenness to produce such a ranking of retrieval results. We propose some initial ideas of a notion of projective betweenness that makes explicit the intuition that two referents should be used when producing a similarity ranking, and indicate how it might be used in relevance feedback.

Citations (2)


... As a result of the search, a ranked list of images is returned to the user. The list is ordered by a degree of similarity, that can be calculated in several ways [7]: Euclidean distance (that is the most used), Cosine similarity, Manhattan distance, χ 2 -square distance, etc [38]. ...

Reference:

Visual descriptors for content-based retrieval of remote sensing images
Similarity: Measurement, Ordering and Betweenness
  • Citing Conference Paper
  • October 2004

Lecture Notes in Computer Science

... In both instances, the words of the caption either reinforce or nullify the meaning of an image, depending on the text-image relationship (Duncum, 2004, p. 256). The importance of context in changing the meaning of an image is supported by Brink, Squire and Bigelow's (2006) findings that users interpret an image differently if it is grouped with other images (p. 716). ...

The Meaning of an Image in Content-Based Image Retrieval