Food-related photos have become increasingly very popular, due to social networks, food recommendation and dietary assessment systems. Reliable annotation is essential in those systems, but user-contributed tags are o...
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ISBN:
(纸本)9781479942732
Food-related photos have become increasingly very popular, due to social networks, food recommendation and dietary assessment systems. Reliable annotation is essential in those systems, but user-contributed tags are often non-informative and inconsistent, and unconstrained automatic food recognition still has relatively low accuracy. Most works focus on exploiting only the visual content while ignoring the context. In this paper, we improve the food image recognition with using two novel components two kinds of context. Firstly, different from the conventional approach representing image in a visual feature space the visual features, we try to represent the images in the a semantic space (also called semantic simplex), where we model aiming at modeling more context information between each categories. Secondly, we explore leveraging leverage the geographic context of the user and information about geolocation and information about restaurants to simplify the classification problem. Thus, We propose a food recognition framework for the food recognition based on these two kinds of context, based on including semantic features learning and location-adaptive classification. We collected a restaurant-oriented food dataset with food images, dish tags and restaurant-level information, such as the menu and geographic location. Experiments on this dataset show that exploiting geolocation improves around 30% the recognition performance, and the semantic feature has a gain of 3%-10% to the other visual features.
With the emergence of new applications, the traditional methods of mining frequent itemsets are confronted with enormous challenges in uncertain environment. As one of the most classic algorithms, Eclat algorithm is r...
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With the emergence of new applications, the traditional methods of mining frequent itemsets are confronted with enormous challenges in uncertain environment. As one of the most classic algorithms, Eclat algorithm is regarded as a promising approach whose efficiency is experimentally proved in mining expected support-based frequent itemsets, while it has not received much attention in the area of mining probabilistic frequent itemsets. In this paper, we review the previous efficient algorithms in the research of frequent itemsets mining over uncertain databases, and then we propose a vertical mining algorithm with Eclat framework, which can mine all probabilistic frequent itemsets exactly and efficiently. In order to study its performance, we design different experiments to evaluate its performance on both synthetic and real data sets, which shows that the vertical mining algorithm is simple to implement and perform well in practice.
At present, the recommendation of information for travel is concentrating on two parts: personalized travel recommendations and classic travel recommendations. However, the interaction relationship between the user pr...
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At present, the recommendation of information for travel is concentrating on two parts: personalized travel recommendations and classic travel recommendations. However, the interaction relationship between the user preference and the classic attractions should also be taken into consideration. Comprehensive the above consideration, this essay propose a more effective and precise algorithm named CIAP (Combination of Interest and Popularity), which based on personal interests and attractions popularity. This algorithm extracts users' interest matrix from users' GPS trajectories to build a core user model. Based on this mode, CAIP defines user similarity function and attractions popular degree function, then CIAP gets the optimal results of recommendation by determining similarity of user's attractions weight and attractions popularity weight. We evaluate the recommended effect of CIAP algorithm in different weight based on GPS data which is collected in the Geolife project (Microsoft Research Asia), that show our method has a better comprehensive performance.
By using link mining method, we can understand associations among persons in enterprises. In this paper, we propose a link mining model called a bi-directional link mining, which is capable of predicting edges that do...
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Anonymous is one of the most important security properties for kinds of Internet applications. In this paper, we consider the privacy-preserving problem in the context of public key broadcast encryption. We provide a ...
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Finding frequent itemsets is computationally the most expensive step in association rules mining, and most of the research attention has been focused on it. With the observation that support plays an important role in...
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Finding frequent itemsets is computationally the most expensive step in association rules mining, and most of the research attention has been focused on it. With the observation that support plays an important role in frequent item mining, in this paper, a conjecture on support count is proved and improvements of traditional Eclat algorithm are presented. The new Bi-Eclat algorithm sorted on support: Items sort in descending order according to the frequencies in transaction cache while itemsets use ascending order of support during support count. Compared with traditional Eclat algorithm, the results of experiments show that the Bi-Eclat algorithm gains better performance on several public databases given. Furthermore, the Bi-Eclat algorithm is applied in analyzing combination principles of prescriptions for Hepatitis B in Traditional Chinese Medicine, which shows its efficiency and effectiveness in practical usefulness.
In this work, we present a new anonymous HIBE scheme which is unbounded in the sense that the public parameters do not impose additional limitations on the functionality of the systems. In most of previous constructio...
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We present a physically-based animation method for simulating the diffusion of water pollutant in this paper. Our method allows animating the dynamic evolution of biological pollutants(e.g., water hyacinth, algae) on ...
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We present a physically-based animation method for simulating the diffusion of water pollutant in this paper. Our method allows animating the dynamic evolution of biological pollutants(e.g., water hyacinth, algae) on largescale water surface effectively. To enable the simulation of such a phenomenon in large scale virtual environment, we simplify the problem by considering the water pollutants as large patches or clusters of aquatic plants rather than each single aquatic plant. Since there exits obvious interface between the large patches of pollutants and the water surface, we model the evolution of the interface in our approach. We use 2D dynamic curves to represent the interface and the diffusion of the water pollutants can be regarded as the dynamic evolution and propagation of 2D curves. To alleviate the topological changes of 2D curves, we adopt a physically-based 2D level set model to animate the evolution and propagation of the interface. We build a level set equation to model the evolution of the interface. In addition, to handle the large scale virtual environment correctly in our physically-based level set model, an imagebased 2D voxelization method is proposed in the paper. In the voxelization method, the virtual environment will be converted to boundary conditions when solving the level set equation. Finally, the water pollutants diffusion phenomenon is simulated on large scale water surface by merging the interface animation results as well as the large scale virtual environment. Animation results about the algae propagation phenomenon in Taihu Lake show that our method is intuitively to be implemented and very convenient to produce visually interesting results.
In crowd animation production, it is difficult to design realistic and natural crowd motion paths using traditional methods. In order to solve the problems in generating paths of a crowd, a new approach based on an in...
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In crowd animation production, it is difficult to design realistic and natural crowd motion paths using traditional methods. In order to solve the problems in generating paths of a crowd, a new approach based on an interactive evolutionary algorithm(EA) named biogeography-based optimization(BBO) is proposed in this paper. The optimization process of individuals is considered as the motion process of a crowd. Specifically, the new approach is applied into a system to generate crowd motion paths and the simulation results demonstrate the efficiency and effectiveness of it.
The outlier detection algorithm based on reverse k-nearest neighbors can detect isolated points. The time complexity of finding the k-nearest neighbor is O(kN2), which is not suitable for large data set, and the selec...
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