Study on predicting sentiment from images using categorical and sentimental keyword-based image retrieval

Visual stimuli are the most sensitive stimulus to affect human sentiments. Many researches have attempted to find the relationship between visual elements in images and sentimental elements using statistical approaches. In many cases, the range of sentiment that affects humans varies with image categories, such as landscapes, portraits, sports, and still life. Therefore, to enhance the performance of sentiment prediction, an individual prediction model must be established for each image category. However, collecting much ground truth sentiment data is one of the obstacles encountered by studies on this field. In this paper, we propose an approach that acquires a training data set for category classification and predicting sentiments from images. Using this approach, we collect a training data set and establish a predictor for sentiments from images. First, we estimate the image category from a given image, and then we predict the sentiment as coordinates on the arousal–valence space using the predictor of an estimated category. We show that the performance of our approach approximates performance using ground truth data. Based on our experiments, we argue that our approach, which utilizes big data on the web as the training set for predicting content sentiment, is useful for practical purposes

Cognitive Designers Activity Study, Formalization, Modelling, and Computation

This study aims to explore how designers mentally categorise design information during the early sketching performed in the generative phase. An action research approach is particularly appropriate for identifying the various sorts of design information and the cognitive operations involved in this phase. Thus, we conducted a protocol study with eight product designers based on a descriptive model derived from cognitive psychological memory theories. Subsequent protocol analysis yielded a cognitive model depicting the mental categorisation of design information processing performed by designers. This cognitive model included a structure for design information (high, middle, and low levels) and linked cognitive operations (association and transformation). Finally, this paper concludes by discussing directions for future research on the development of new computational tools for designers

Fuzzy aesthetic semantics description and extraction for art image retrieval

AbstractMore and more digitized art images are accumulated and expanded in our daily life and techniques are needed to be established on how to organize and retrieve them. Though content-based image retrieval (CBIR) made great progress, current low-level visual information based retrieval technology in CBIR does not allow users to search images by high-level semantics for art image retrieval. We propose a fuzzy approach to describe and to extract the fuzzy aesthetic semantic feature of art images. Aiming to deal with the subjectivity and vagueness of human aesthetic perception, we utilize the linguistic variable to describe the image aesthetic semantics, so it becomes possible to depict images in linguistic expression such as ‘very action’. Furthermore, we apply neural network approach to model the process of human aesthetic perception and to extract the fuzzy aesthetic semantic feature vector. The art image retrieval system based on fuzzy aesthetic semantic feature makes users more naturally search desired images by linguistic expression. We report extensive empirical studies based on a 5000-image set, and experimental results demonstrate that the proposed approach achieves excellent performance in terms of retrieval accuracy

COMBINING INDEPENDENT COMPONENT ANALYSIS WITH CHAOTIC QUANTIFIERS FOR THE RECOGNITION OF POSITIVE, NEGATIVE AND NEUTRAL EMOTIONS USING EEG SIGNALS

ABSTRACT Given the importance of recognizingemotions, the present study attempts to recognize emotions from the EEG signals. The main idea of this study is that the brain has independent sources with different functions. Thus, emotions would be observable in independent brain sources. These sources are obtained by Independent component Analysis (ICA) algorithm from recorded EEG signals. However, considering the ill-posed problem in ICA, the Shannon entropy was used to resolve this problem and sort outthe sources. Moreover, Recurrence Quantification Analysis (RQA) was used to extract chaotic features of each source and then, using a k-nearest neighbor (KNN) Classifier, the chaotic features of the three types of emotional state, i.e., positive, negative and neutral were analyzed, which yielded significant results. The results suggested that the greatest difference was observed in lowentropy sources while high-entropy sources showed no significant changes. Finally, for each emotional state, we established a relation between emotions and sources

The Role of Visual Rhetoric in Semantic Multimedia: Strategies for Decision Making in Times of Crisis

As semantic multimedia is approaching mainstream, even the great improvements that can be seen in its classic schools, like the data mining inspired Information Retrieval based on metadata analysis, or Computer Vision, might not be enough. We identify a new group that gains traction in the semantic multimedia community and which uses as starting point developments from psychology and visual communication. For the purposes of this article we restrict our domain to visual rhetoric as we consider it to yield the biggest potential for future developments. Living in times when the periods between crises seem to be shorter and shorter, we look at how developments in semantic multimedia can be used for predicting and overcoming crises. We analyze at least 2 aspects related to this: using information visualization to understand the evolution of crises and creating multi-layered semantic multimedia technologies that can easily be adapted to use a variety of sources and solve problems from different domains. In both cases we show how techniques inspired by visual rhetoric (information linking, framing, composition) in conjunction with named entity recognition offer a lot of benefits. The section related to multi-layered semantic multimedia technologies also draws on the lessons learned while designing a prototype application aimed at improving tourism decision making process. The article ends with a discussion on evaluation methods for multi-layered semantic technologies applications. We look at how to evaluate them on both levels: mechanisms (information linking versus raw named entity recognition when generating visuals, for example), and decision making strategies (Do such systems actually solve real problems related to crises, create jobs or at least can they be repurposed to solve other problems than the one with which we have started?)

Модели, методи и алгоритми за създаване на система за управление на мултимедийно съдържание

ИМИ-БАН, 24.09.2012 г., присъждане на образователна и научна степен "доктор" на Янислав Панайотов Желев по научна специалност 01.01.12 информатика. [Zhelev Yanislav Panayotov; Желев Янислав Панайотов