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In this work, we target at solving the Bing challenge provided by Microsoft. The task is to design an effective and efficient measurement of query terms in describing the images (image-query pairs) crawled from the web. We observe that the provided image-query pairs (e.g., text-based image retrieval results) are usually related to their surrounding text; however, the relationship between image content seems to be ignored. Hence, we attempt to integrate the visual information for better ranking results. In addition, we found that plenty of query terms are related to people (e.g., celebrity) and user might have similar queries (click logs) in the search engine. Therefore, in this work, we propose a relevance association by investigating the effectiveness of different auxiliary contextual cues (i.e., face, click logs, visual similarity). Experimental results show that the proposed method can have 16% relative improvement compared to the original ranking results. Especially, for people-related queries, we can further have 45.7% relative improvement.

Recently, smart phones not only perform the basic communication function but also become the first choice in information collection. For instance, when smartphone users want to obtain relevant information about the products on the shelf, all they have to do is take a snapshot and send it back to the server. In order to save time and effort for the users, it is important to retrieve information as many as possible from one shot. Thus, multiple object recognition and localization over large-scale object classes (database) is the first bottleneck to break through. To tackle this issue, we propose a bottom up search-based approach, which localizes the grid-based search candidates in Markov Random Field (MRF). The proposed approach enables simultaneously recognizing and localizing multiple objects; therefore, it reduces response time and ensures the accuracy as well. Experimental results show that the proposed method can have 40% relative improvement over the state-of-the-art bag-of-words model. We also demonstrate the proposed method in two datasets and show that our method can have good improvement in running time (5 times faster), and also competitive accuracy for multi-object recognition and localization.

This work attempts to tackle the IBM grand challenge -seeing the daily life of New York City (NYC) in various perspectives by exploring rich and diverse social media content. Most existing works address this problem relying on single media source and covering limited life aspects. Because different social media are usually chosen for specific purposes, multiple social media mining and integration are essential to understand a city comprehensively. In this work, we first discover the similar and unique natures (e.g., attractions, topics) across social media in terms of visual and semantic perceptions. For example, Instagram users share more food and travel photos while Twitter users discuss more about sports and news. Based on these characteristics, we analyze a broad spectrum of life aspects -trends, events, food, wearing and transportation in NYC by mining a huge amount of diverse and freely available media (e.g., 1.6M Instagram photos, 5.3M Twitter posts). Because transportation logs are hardly available in social media, the NYC Open Data (e.g., 6.5B subway station transactions) is leveraged to visualize temporal traffic patterns. Furthermore, the experiments demonstrate that our approaches can effectively overview urban life with considerable technical improvement, e.g., having 16% relative gains in food recognition accuracy by a hierarchy crossmedia learning strategy, reducing the feature dimensions of sentiment analysis by 10 times without sacrificing precision.

This paper presents the design and simulation results of a high-precision low-cost refractometer that demonstrates the main advantage of a wide measurement range (1 ≤ n ≤ 2). The proposed design is based on the diffractive properties of sub-micron gratings and Snell's Law. The precision and uncertainty factors of the proposed system were tested and analyzed, revealing that the proposed refractometer demonstrates a wide measurement range with sensitivity of 10−4.