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Advances in Multimedia Information Processing – PCM 2014: by Wei Tsang Ooi, Cees G.M. Snoek, Hung Khoon Tan, Chin Kuan

By Wei Tsang Ooi, Cees G.M. Snoek, Hung Khoon Tan, Chin Kuan Ho, Benoit Huet, Chong-Wah Ngo

This ebook constitutes the refereed complaints of the fifteenth Pacific Rim convention on Multimedia, PCM 2014, held in Kuching, Malaysia, in December 2014. The 35 revised complete papers and six brief papers provided have been rigorously reviewed and chosen from eighty four submissions. The papers hide quite a lot of subject matters within the zone of multimedia content material research, multimedia sign processing and communications, and multimedia functions and companies. they've been equipped into topical sections on video coding, annotation, picture and photograph, functions, humans, picture research and processing less than additional support, nearest neighbor, neural networks, and audio. additionally incorporated are sections with top papers and posters and demonstrations.

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Additional info for Advances in Multimedia Information Processing – PCM 2014: 15th Pacific-Rim Conference on Multimedia, Kuching, Malaysia, December 1-4, 2014, Proceedings

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Examples of “omelet” photos. Most of them have drawings drawn by ketchup. and “omelet with fried rice (called as Ome-rice in Japanese)” are also popular foods in Japan. The results of twitter food image mining reflects food preference of Japanese people. In addition, we found that many of “ome-rice” had drawings or letters drawn with ketchup, as shown in Figure 3. These are estimated to be made at home, while most of “ramen” and “sushi” photos are taken at food restaurants, because there are many ramen noodle and sushi restaurant in Japan.

Here we assume that the Improving Color Constancy with Internet Photo Collections 35 illumination is constant throughout the whole scene, so E(λ ) is independent of the image coordinate position x. qc (λ ) is a camera sensor function. Tan[1] defines image chromaticity σ c (x) , diffuse chromaticity Λc (x) , and specular chromaticity (or illumination chromaticity) Γc as follows: σ c (x) = I c (x) where I cD (x) =  Ω  I (x),Λ (x) = I i c D c (x) I D i (x) ,Γ c = I cS I S i (2) Sd (λ , x)E (λ ) q c (λ ) d λ and I cS =  E (λ ) q c (λ ) d λ .

Thus, introducing the prior can provide a good initial guess for the successive iteration. In the next, a specular-map guided filter is used to improve the precision of specular edge weighting. Consequently, it can increase the accuracy of estimating the illuminant. To the end, we evaluate our scheme on standard databases and the results show that the proposed scheme outperforms state of the art color constancy methods. Keywords: Visual context, color constancy, specular map, guilded filter. 1 Introduction A computational approach to recover the actual color of objects is referred to as color constancy [1].

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