2009 15th International Conference on Virtual Systems and Multimedia 2009
DOI: 10.1109/vsmm.2009.28
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Translating Journalists' Requirements into Features for Image Search

Abstract: Abstract-This paper illustrates how taking advantage of user studies highlighting the user requirements can lead to the selection of suitable visual features in image search systems. The results of a study to identify pertinent visual features to enhance a text-based press photo search system used by journalists are presented. A requirement was that the visual features should be intuitively understandable by the journalists. This feature selection task is approached by first determining the journalists' photo … Show more

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Cited by 9 publications
(5 citation statements)
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References 16 publications
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“…We introduce features based on the experimentally-determined relation between color saturation and brightness, and emotion dimensions [26], as well as features based on relations between color combinations and induced emotional effects from art theory [15]. We complement these features by a selection of features, some of which are shown to be of use in similar image retrieval [24] and classification tasks [7,32]. In this work, features representing the color, texture, composition and content were implemented.…”
Section: Featuresmentioning
confidence: 99%
“…We introduce features based on the experimentally-determined relation between color saturation and brightness, and emotion dimensions [26], as well as features based on relations between color combinations and induced emotional effects from art theory [15]. We complement these features by a selection of features, some of which are shown to be of use in similar image retrieval [24] and classification tasks [7,32]. In this work, features representing the color, texture, composition and content were implemented.…”
Section: Featuresmentioning
confidence: 99%
“…In 2010 Machajdik and Hanbury [30] performed an intensive study on image emotion classification by properly combining the use of several low and high visual features. These features have been obtained by exploiting concepts from and art theory [12, 16], or exploited in image retrieval [78] and image classification [9, 17] tasks. They selected 17 visual features, categorised into four groups: Colour: mean saturation and brightness, 3D emotion representation by Valdez and Mehrabian [16], hue statistics, colourfulness measure according to [17], number of pixels of each of the 11 basic colours [79], Itten contrast [12], colour histogram designed by Wei‐ning et al [9].…”
Section: Visual Sentiment Analysis Systemsmentioning
confidence: 99%
“…It has been used not only on images but also on probability distribution functions used for the stochastic watershed of multipectral images [2] or on triangular meshes [9]. The domain of application is broad going from multimedia images [21] to SAR imagery [19].…”
Section: Introductionmentioning
confidence: 99%