Research on multi-modal sentiment feature learning of social media content

社交媒体已成为现代社会舆论交流和信息传递的主要平台。针对社交媒体的情感分析对于舆论监控、商业产品导向和股市预测等都具有重大应用价值。但社交媒体内容的多模态性（文本、图片等）让传统的单模态情感分析方法面临许多局限，多模态情感分析技术对跨媒体内容的理解与分析具有重大的理论价值。 多模态情感分析区别于单模态方法的关键问题在于，如何综合利用形态各异的多模态情感信息，来获取整体的情感倾向性，同时考虑单个模态本身在情感表达上的性质。针对该问题，利用社交媒体上的多模态内容在情感表达上所具有的关联性、抽象层级性的特点，提出了一套面向社交媒体的多模态情感特征学习与融合方法，实现多模态情感分析，主要内容和创新点...Social media has become a main platform of public communication and information transmission. Therefore, social media sentiment analysis has great application values in many fields, such as public opinion monitoring, production marking, stock forecasting and so on. But the multi-modal characteristic of social media content (e.g. texts and images) significantly challenges traditional text-based sen...学位：工学硕士院系专业：信息科学与技术学院_模式识别与智能系统学号：3152013115327

How do users make a people-centric slideshow?

This paper presents a pilot user study that attempts to shed light on the ways users create people-centric slideshows, with the objective of scaling it up to a crowdsourcing experiment. The study focuses on two major directions, namely image selection and image sequencing. Participants were asked to select photos of a specific person from an initial set and arrange them into a slideshow. Results show that there is correlation between specific predictors and selected images, as well as their relative position in the final sequence. This indicates that a crowdsourcing experiment will indeed high-light the characteristics of the average user, which can then be incorporated into an automatic people-centric slideshow creator. Categories and Subject Descriptor

Browse-to-search

This demonstration presents a novel interactive online shopping application based on visual search technologies. When users want to buy something on a shopping site, they usually have the requirement of looking for related information from other web sites. Therefore users need to switch between the web page being browsed and other websites that provide search results. The proposed application enables users to naturally search products of interest when they browse a web page, and make their even causal purchase intent easily satisfied. The interactive shopping experience is characterized by: 1) in session - it allows users to specify the purchase intent in the browsing session, instead of leaving the current page and navigating to other websites; 2) in context - -the browsed web page provides implicit context information which helps infer user purchase preferences; 3) in focus - users easily specify their search interest using gesture on touch devices and do not need to formulate queries in search box; 4) natural-gesture inputs and visual-based search provides users a natural shopping experience. The system is evaluated against a data set consisting of several millions commercial product images. © 2012 Authors

Decision Fusion in Non-stationary Environments

A parallel distributed detection system consists of multiple local sensors/detectors that observe a phenomenon and process the gathered observations using inbuilt processing capabilities. The end product of the local processing is transmitted from each sensor/detector to a centrally located data fusion center for integration and decision making. The data fusion center uses a specific optimization criterion to obtain global decisions about the environment seen by the sensors/detectors. In this study, the overall objective is to make a globally-optimal binary (target/non-target) decision with respect to a Bayesian cost, or to satisfy the Neyman-Pearson criterion. We also note that in some cases a globally-optimal Bayesian decision is either undesirable or impractical, in which case other criteria or localized decisions are used. In this thesis, we investigate development of several fusion algorithms under different constraints including sequential availability of data and dearth of statistical information. The main contribution of this study are: (1) an algorithm that provides a globally optimal solution for local detector design that satisfies a Neyman-Pearson criterion for systems with identical local sensors; (2) an adaptive fusion algorithm that fuses local decisions without a prior knowledge of the local sensor performance; and (3) a fusion rule that applies a genetic In addition, we develop a parallel decision fusion system where each local sensor is a sequential decision maker that implements the modified Wald's sequential probability test (SPRT) as proposed by Lee and Thomas (1984).Ph.D., Electrical Engineering -- Drexel University, 201

Emotion-based sequence of family photos

This paper presents a method for the automatic creation of slideshows from family photo collections based on the emotions of a given group of people. The user specifies the desired person(s) to be included in the slideshow. A natural image sequence is formed based on people’s emotions and several other, user-defined image similarity attributes, in order to form meaningful slideshow transitions. This process makes use of a new image dissimilarity function, which can integrate various attribute combinations and preferences, making the system highly user-adaptable and flexible