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Proceedings ArticleDOI

UCID: an uncompressed color image database

TLDR
A new dataset, UCID (pronounced "use it") - an Uncompressed Colour Image Dataset which tries to bridge the gap between standardised image databases and objective evaluation of image retrieval algorithms that operate in the compressed domain.
Abstract
Standardised image databases or rather the lack of them are one of the main weaknesses in the field of content based image retrieval (CBIR). Authors often use their own images or do not specify the source of their datasets. Naturally this makes comparison of results somewhat difficult. While a first approach towards a common colour image set has been taken by the MPEG 7 committee 1 their database does not cater for all strands of research in the CBIR community. In particular as the MPEG-7 images only exist in compressed form it does not allow for an objective evaluation of image retrieval algorithms that operate in the compressed domain or to judge the influence image compression has on the performance of CBIR algorithms. In this paper we introduce a new dataset, UCID (pronounced ”use it”) - an Uncompressed Colour Image Dataset which tries to bridge this gap. The UCID dataset currently consists of 1338 uncompressed images together with a ground truth of a series of query images with corresponding models that an ideal CBIR algorithm would retrieve. While its initial intention was to provide a dataset for the evaluation of compressed domain algorithms, the UCID database also represents a good benchmark set for the evaluation of any kind of CBIR method as well as an image set that can be used to evaluate image compression and colour quantisation algorithms.

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Citations
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Proceedings ArticleDOI

Removing Rain from Single Images via a Deep Detail Network

TL;DR: A deep detail network is proposed to directly reduce the mapping range from input to output, which makes the learning process easier and significantly outperforms state-of-the-art methods on both synthetic and real-world images in terms of both qualitative and quantitative measures.
Journal ArticleDOI

Image De-Raining Using a Conditional Generative Adversarial Network

TL;DR: This work attempts to leverage powerful generative modeling capabilities of the recently introduced conditional generative adversarial networks (CGAN) by enforcing an additional constraint that the de-rained image must be indistinguishable from its corresponding ground truth clean image.
Journal ArticleDOI

Clearing the Skies: A Deep Network Architecture for Single-Image Rain Removal

TL;DR: Zhang et al. as mentioned in this paper introduced a deep network architecture called DerainNet for removing rain streaks from an image, which directly learned the mapping relationship between rainy and clean image detail layers from data.
Journal ArticleDOI

Features for image retrieval: an experimental comparison

TL;DR: An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented and the often used, but very simple, color histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications.
Proceedings ArticleDOI

Removing Rain from a Single Image via Discriminative Sparse Coding

TL;DR: The paper aims at developing an effective algorithm to remove visual effects of rain from a single rainy image, i.e. separate the rain layer and the de-rained image layer from an rainy image through a dictionary learning based algorithm.
References
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Proceedings ArticleDOI

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Columbia Object Image Library (COIL100)

S. Nayar
TL;DR: Columbia Object Image Library COIL is a database of color images of objects that were placed on a motorized turntable against a black background and rotated through degrees to vary object pose with respect to a xed color camera.
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