We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360 0 videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.

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We propose a multiclass (MC) classification approach to text categorization (TC). To fully take advantage of both positive and negative training examples, a maximal figure-of-merit (MFoM) learning algorithm is introduced to train high performance MC classifiers. In contrast to conventional binary classification, the proposed MC scheme assigns a uniform score function to each category for each given test sample, and thus the classical Bayes decision rules can now be applied. Since all the MC MFoM classifiers are simultaneously trained, we expect them to be more robust and work better than the binary MFoM classifiers, which are trained separately and are known to give the best TC performance. Experimental results on the Reuters-21578 TC task indicate that the MC MFoM classifiers achieve a micro-averaging F 1 value of 0.377, which is significantly better than 0.138, obtained with the binary MFoM classifiers, for the categories with less than 4 training samples. Furthermore, for all 90 categories, most with large training sizes, the MC MFoM classifiers give a micro-averaging F 1 value of 0.888, better than 0.884, obtained with the binary MFoM classifiers.

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This paper presents a finite element method-based framework for an object withering simulation modeled with heterogeneous material, such as fruits drying or decay. We introduce diffusion procedures for both the moisture content and decay spread, which are solved directly on a tetrahedral mesh representation of the fruit flesh. Then, we use the moisture content to control shrinkage through the initial strain, which is integrated into the Lagrangian dynamic equation, and solved with the finite element method. For the complex structure of the object, another fine triangle mesh is used to represent the skin, and its deformation is solved by a thin shell technique. To couple the motion between different layers of the fruit, a tracking force is used to pull the skin and drive its deformation together with the volume mesh. In comparison with the previous work, our method provides temporally and spatially varying parameters to model the complex phenomena of object withering. Moreover, the water diffusivity can also be given by user input to present various material properties of the cut section and skin-covered area. Our algorithm is easy to implement and highly efficient in generating a realistic appearance for the withering effect. For a medium-scale model, we can achieve interactive simulation. Copyright