An Eye for Art: Focusing on Great Artists and Their Work by National Gallery of Art
By National Gallery of Art
Lavishly illustrated with countless numbers of full-color photographs, this family-oriented paintings source introduces little ones to greater than 50 nice artists and their paintings, with corresponding actions and explorations that encourage creative improvement, targeted having a look, and artistic writing. This treasure trove of art from the nationwide Gallery of paintings comprises, between others, works by way of Raphael, Rembrandt, Georgia O'Keeffe, Henri Matisse, Chuck shut, Jacob Lawrence, Pablo Picasso, and Alexander Calder, representing a variety of inventive kinds and methods. Written by means of museum educators with a long time of hands-on adventure in either art-making actions and making artwork relatable to young children, the actions comprise sculpting a clay determine encouraged via Edgar Degas; drawing an item from contact by myself, encouraged via Joan Miro's event as an artwork scholar; portray a double-sided portrait with one facet reflecting actual qualities and the opposite facet character features, encouraged via Leonardo da Vinci's Ginevra de' Benci; and making a tale in accordance with a Mary Cassatt portray. Educators, homeschoolers, and households alike will locate their creativity sparked through this paintings extravaganza.
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Extra resources for An Eye for Art: Focusing on Great Artists and Their Work
This chapter investigates three types of recommendation systems (shown in Figures 9, 10, and 11). Fig. 9 Content-meta-based search Fig. 10 Context-aware search Fig. 11 User-preference-based search 2 Cross-category Recommendation for Multimedia Content 41 1) Content-meta-based Search In content-meta-based search, users can acquire relevant content by querying for keywords or features. The content data are initially indexed based on keywords or features by employing a content profiling process.
1) VSM One of the simplest approaches for similarity calculation is using the VSM. This model measures the distance between vectors. The most practical distance measure is the cosine distance, as shown in Figure 7. For example, user preference (UP) and Fig. 7 Example of Similarity in VSM 38 N. Kamimaeda et al. content profile (CP) are expressed as an n-dimensional feature vector in the VSM. c1 ; c2 ; ; ; cn / contents profile vector 2) NB Classifier NB is a probabilistic approach to classify data or infer a hypothesis.
P. 393-399. 6. D. O’Sullivan, B. Smyth, D. C. Wilson, K. McDonald, and A. Smeaton, “Improving the Quality of the Personalized Electronic Program Guide,” User Modeling and User Adapted Interaction;Vol. 14, No. p. 5-36. 7. S. Gutta, K. Kuparati, K. Lee, J. Martino, D. Schaffer, and J. p. 1121-1122. 8. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. p. 175-186. 1 Personalized Movie Recommendation 25 9. J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl, “GroupLens: Applying Collaborative Filtering to Usenet News,” Communications of the ACM, Vol.