96 research outputs found
Horror image recognition based on context-aware multi-instance learning
Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the Fuzzy Support Vector Machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large scale image sets collected from the Internet
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
Existing methods on visual emotion analysis mainly focus on coarse-grained
emotion classification, i.e. assigning an image with a dominant discrete
emotion category. However, these methods cannot well reflect the complexity and
subtlety of emotions. In this paper, we study the fine-grained regression
problem of visual emotions based on convolutional neural networks (CNNs).
Specifically, we develop a Polarity-consistent Deep Attention Network (PDANet),
a novel network architecture that integrates attention into a CNN with an
emotion polarity constraint. First, we propose to incorporate both spatial and
channel-wise attentions into a CNN for visual emotion regression, which jointly
considers the local spatial connectivity patterns along each channel and the
interdependency between different channels. Second, we design a novel
regression loss, i.e. polarity-consistent regression (PCR) loss, based on the
weakly supervised emotion polarity to guide the attention generation. By
optimizing the PCR loss, PDANet can generate a polarity preserved attention map
and thus improve the emotion regression performance. Extensive experiments are
conducted on the IAPS, NAPS, and EMOTIC datasets, and the results demonstrate
that the proposed PDANet outperforms the state-of-the-art approaches by a large
margin for fine-grained visual emotion regression. Our source code is released
at: https://github.com/ZizhouJia/PDANet.Comment: Accepted by ACM Multimedia 201
Weakly supervised coupled networks for visual sentiment analysis
Automatic assessment of sentiment from visual content
has gained considerable attention with the increasing tendency
of expressing opinions on-line. In this paper, we solve
the problem of visual sentiment analysis using the high-level
abstraction in the recognition process. Existing methods
based on convolutional neural networks learn sentiment
representations from the holistic image appearance. However,
different image regions can have a different influence
on the intended expression. This paper presents a weakly
supervised coupled convolutional network with two branches
to leverage the localized information. The first branch
detects a sentiment specific soft map by training a fully convolutional
network with the cross spatial pooling strategy,
which only requires image-level labels, thereby significantly
reducing the annotation burden. The second branch utilizes
both the holistic and localized information by coupling
the sentiment map with deep features for robust classification.
We integrate the sentiment detection and classification
branches into a unified deep framework and optimize
the network in an end-to-end manner. Extensive experiments
on six benchmark datasets demonstrate that the
proposed method performs favorably against the state-ofthe-
art methods for visual sentiment analysis
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
Visual sentiment prediction based on automatic discovery of affective regions
Automatic assessment of sentiment from visual
content has gained considerable attention with the increasing
tendency of expressing opinions via images and videos online.
This paper investigates the problem of visual sentiment analysis,
which involves a high-level abstraction in the recognition process.
While most of the current methods focus on improving holistic
representations, we aim to utilize the local information, which is
inspired by the observation that both the whole image and local
regions convey significant sentiment information. We propose
a framework to leverage affective regions, where we first use
an off-the-shelf objectness tool to generate the candidates, and
employ a candidate selection method to remove redundant and
noisy proposals. Then a convolutional neural network (CNN) is
connected with each candidate to compute the sentiment scores,
and the affective regions are automatically discovered, taking the
objectness score as well as the sentiment score into consideration.
Finally, the CNN outputs from local regions are aggregated with
the whole images to produce the final predictions. Our framework
only requires image-level labels, thereby significantly reducing
the annotation burden otherwise required for training. This is
especially important for sentiment analysis as sentiment can be
abstract, and labeling affective regions is too subjective and
labor-consuming. Extensive experiments show that the proposed
algorithm outperforms the state-of-the-art approaches on eight
popular benchmark datasets
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
WSCNet: Weakly Supervised Coupled Networks for Visual Sentiment Classification and Detection
Automatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions online. In this paper, we solve the problem of visual sentiment analysis, which is challenging due to the high-level abstraction in the recognition process. Existing methods based on convolutional neural networks learn sentiment representations from the holistic image, despite the fact that different image regions can have different influence on the evoked sentiment. In this paper, we introduce a weakly supervised coupled convolutional network (WSCNet). Our method is dedicated to automatically selecting relevant soft proposals from weak annotations (e.g., global image labels), thereby significantly reducing the annotation burden, and encompasses the following contributions. First, WSCNet detects a sentiment-specific soft map by training a fully convolutional network with the cross spatial pooling strategy in the detection branch. Second, both the holistic and localized information are utilized by coupling the sentiment map with deep features for robust representation in the classification branch. We integrate the sentiment detection and classification branches into a unified deep framework, and optimize the network in an end-to-end way. Through this joint learning strategy, weakly supervised sentiment classification and detection benefit each other. Extensive experiments demonstrate that the proposed WSCNet outperforms the state-of-the-art results on seven benchmark datasets
Enhanced Living Environments
This open access book was prepared as a Final Publication of the COST Action IC1303 “Algorithms, Architectures and Platforms for Enhanced Living Environments (AAPELE)”. The concept of Enhanced Living Environments (ELE) refers to the area of Ambient Assisted Living (AAL) that is more related with Information and Communication Technologies (ICT). Effective ELE solutions require appropriate ICT algorithms, architectures, platforms, and systems, having in view the advance of science and technology in this area and the development of new and innovative solutions that can provide improvements in the quality of life for people in their homes and can reduce the financial burden on the budgets of the healthcare providers. The aim of this book is to become a state-of-the-art reference, discussing progress made, as well as prompting future directions on theories, practices, standards, and strategies related to the ELE area. The book contains 12 chapters and can serve as a valuable reference for undergraduate students, post-graduate students, educators, faculty members, researchers, engineers, medical doctors, healthcare organizations, insurance companies, and research strategists working in this area
ICS Materials. Towards a re-Interpretation of material qualities through interactive, connected, and smart materials.
The domain of materials for design is changing under the influence of an increased technological
advancement, miniaturization and democratization. Materials are becoming connected,
augmented, computational, interactive, active, responsive, and dynamic. These are ICS
Materials, an acronym that stands for Interactive, Connected and Smart. While labs around the
world are experimenting with these new materials, there is the need to reflect on their
potentials and impact on design. This paper is a first step in this direction: to interpret and
describe the qualities of ICS materials, considering their experiential pattern, their expressive sensorial dimension, and their aesthetic of interaction. Through case studies, we analyse and classify these emerging ICS Materials and identified common characteristics, and challenges, e.g. the ability to change over time or their programmability by the designers and users. On that basis, we argue there is the need to reframe and redesign existing models to describe ICS materials, making their qualities emerge
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