32,637 research outputs found
Colour constancy using von Kries transformations: colour constancy "goes to the Lab"
Colour constancy algorithms aim at correcting colour towards a correct perception within
scenes. To achieve this goal they estimate a white point (the illuminant's colour), and correct
the scene for its in uence. In contrast, colour management performs on input images colour
transformations according to a pre-established input pro le (ICC pro le) for the given con-
stellation of input device (camera) and conditions (illumination situation). The latter case
presents a much more analytic approach (it is not based on an estimation), and is based on
solid colour science and current industry best practises, but it is rather in exible towards cases
with altered conditions or capturing devices. The idea as outlined in this paper is to take up
the idea of working on visually linearised and device independent CIE colour spaces as used
in colour management, and to try to apply them in the eld of colour constancy. For this
purpose two of the most well known colour constancy algorithms White Patch Retinex and
Grey World Assumption have been ported to also work on colours in the CIE LAB colour
space. Barnard's popular benchmarking set of imagery was corrected with the original imple-
mentations as a reference and the modi ed algorithms. The results appeared to be promising,
but they also revealed strengths and weaknesses
How can heat maps of indexing vocabularies be utilized for information seeking purposes?
The ability to browse an information space in a structured way by exploiting
similarities and dissimilarities between information objects is crucial for
knowledge discovery. Knowledge maps use visualizations to gain insights into
the structure of large-scale information spaces, but are still far away from
being applicable for searching. The paper proposes a use case for enhancing
search term recommendations by heat map visualizations of co-word
relation-ships taken from indexing vocabulary. By contrasting areas of
different "heat" the user is enabled to indicate mainstream areas of the field
in question more easily.Comment: URL workshop proceedings: http://ceur-ws.org/Vol-1311
Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm
In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms
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