Sensations and psychological effects in color image database

Bimbo, A. Del; Pala, Pietro

doi:10.1109/icip.1997.647974

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…Papers by Berretti et al [1], and Corridoni et al [6] [5], were among the earliest in this research field. They used clustering in the CIELUV color space, together with a modified k-means algorithm, for segmenting images into homogenous color regions.…”

Section: Related Workmentioning

confidence: 98%

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Emotion related structures in large image databases

Solli¹,

Lenz²

2010

Proceedings of the ACM International Conference on Image and Video Retrieval

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We introduce two large databases consisting of 750 000 and 1.2 million thumbnail-sized images, labeled with emotionrelated keywords. The smaller database consists of images from Matton Images, an image provider. The larger database consists of web images that were indexed by the crawler of the image search engine Picsearch. The images in the Picsearch database belong to one of 98 emotion related categories and contain meta-data in the form of secondary keywords, the originating website and some view statistics. We use two psycho-physics related feature vectors based on the emotional impact of color combinations, the standard RGBhistogram and two SIFT-related descriptors to characterize the visual properties of the images. These features are then used in two-class classification experiments to explore the discrimination properties of emotion-related categories. The clustering software and the classifiers are available in the public domain, and the same standard configurations are used in all experiments. Our findings show that for emotional categories, descriptors based on global image statistics (global histograms) perform better than local image descriptors (bag-of-words models). This indicates that contentbased indexing and retrieval using emotion-based approaches are fundamentally different from the dominant object-recognition based approaches for which SIFT-related features are the standard descriptors.

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Section: Related Workmentioning

confidence: 98%

“…Given training examples x i ∈ R n , with corresponding labels y i ∈ {−1, 1}, and the decision function f (x) of the SVM. The category probability Pr(y = 1|x) can be approximated by the sigmoid function Pr(y = 1|x) = 1 1 + exp(Af (x) + B) (1) where A and B are estimated by solving…”

Section: Probability Estimatesmentioning

confidence: 99%

Emotion related structures in large image databases

Solli¹,

Lenz²

2010

Proceedings of the ACM International Conference on Image and Video Retrieval

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“…Many early experimental works is present in Beretti et al [11], Corridoni et al [12] and Corridoni et al [13]. Clustering with a modified K-means algorithm has been used for image segmentation resulting into some homogenous color regions [14].…”

Section: B Color Semantics For Systems Of Image Retrievalmentioning

confidence: 99%

An Efficient Indexing Algorithm for CBIR

Rahmani

Ansari

Goel³

2015

2015 IEEE International Conference on Computational Intelligence &Amp; Communication Technology

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Due to the continuous development of high quality multimedia technologies and rapid growth in the computational power along with availability of huge sized storage devices, digital image archives of very large size are being created day by day on the ever growing WWW through many commercial, research & development and academic web sites. The bulk of digitized images over the Internet are attracting significant research efforts for the development of tools to manage the visual data with their fast and effective retrieval. Towards the beginning of the previous decade, the breakthrough techniques called content based image retrieval has emerged in image retrieval field. This technique uses the contents of the image data for segmenting, indexing, retrieval and searching of relevant images from image repository. This paper mainly concentrates on the indexing phase of the image retrieval system for development of an efficient indexing algorithm of CBIR systems.

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“…We can illustrate it for 8000 images on a small database. The findings are implemented in a public which are available for demo search engine [1] , is very important where readers can interact with the search engine. While continuing, we should not except the new technique to deliver semantic indexing for possible image.…”

Section: Introductionmentioning

confidence: 99%