Open Access
Cluster Oriented Image Retrieval System
Mahip M. Bartere,Prashant R. Deshmukh +1 more
- 21 Apr 2012
- Iss: 3, pp 25-27
TL;DR: An evaluation framework for comparing the influence of THE distance function by image mining by color and also a way to mine an image from its name is proposed.
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Abstract: Image mining presents special characteristics due to the richness of data that an image can show. Effective evaluation of results of image mining by content requires that the user point of view is used on the performance parameters. Comparison among different mining by similarity systems is particularly challenging owing to the great variety of methods implemented to represent likeness and the dependence that the result present of the used image set. Other obstacle is lag of parameter for comparing experimental performance. In this paper we propose an evaluation framework for comparing the influence of THE distance function by image mining by color and also a way to mine an image from its name. Experiments with color similarity mining by quantization on color space and measure of likeness between a sample and the image results have been carried out to illustrate the proposed scheme. Important aspects of this type of mining are also described. KeywordsBased Image segmentation, Deviation Factor, Image comparison, Clustering, Text Based Mining.
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Citations
Text Mining for Korean: Characteristics and Application to 2011 Korean Economic Census Data
Juna Goo,Kyunga Kim +1 more
- 31 Dec 2014
TL;DR: In this article, the 2011 Korean Economic Census is the first economic census in Korea, which contains text data on menusserved by Korean-food restaurants as well as structured data on characteristics of restaurants including area, opening year and total sales.
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Graphical causal inference and copula regression model for apple keywords by text mining
Jong Min Kim,Sunghae Jun +1 more
TL;DR: The technological trends and relations between Apple's technologies are shown to make contributions in finding vacant technology areas and central technologies for Apple's R&D planning.
References
Color indexing
Michael J. Swain,Dana H. Ballard +1 more
TL;DR: In this paper, color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models, and they can differentiate among a large number of objects.
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Content based image retrieval systems
TL;DR: A content-based image retrieval (CBIR) system is required to effectively and efficiently use information from these image repositories as discussed by the authors, which helps users (even those unfamiliar with the database) retrieve relevant images based on their contents.
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Comparative analysis of the quantization of color spaces on the basis of the CIELAB color-difference formula
B. Hill,Th. Roger,F. W. Vorhagen +2 more
TL;DR: The CIELAB color spave within the limits of optimal colors including the complete volume of object colors is discussed, which is composed of planes of constant lightness L* with an net of lines parallel to the a* and b* axes.
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Image retrieval based on color features: an evaluation study
TL;DR: In this paper, the authors present an experimental evaluation of different image content representations, all of which are based on the use of color histograms to support indexing and searching schemes.
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Bounds for the discrimination power of color indexing techniques
TL;DR: The set of parameters for which color indexing works well can be described as the set of Parameters for which the maximal match number is below an application-dependent maximum.
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