Classification and Characteristics of Landscape in Pusan Using the Landscape Picture Database

1997 ◽  
Vol 32 (0) ◽  
pp. 301-306
Author(s):  
Kyunghee Kim ◽  
Seiji Sato ◽  
Takahumi Arima ◽  
Naishen Hsiao ◽  
Keiichiro Hitaka ◽  
...  
Keyword(s):  
Picture Database

scholarly journals Implementation of a New Food Picture Database in the Context of fMRI and Visual Cognitive Food-Choice Task in Healthy Volunteers

2019 ◽  
Vol 10 ◽  
Author(s):  
Yentl Gautier ◽  
Paul Meurice ◽  
Nicolas Coquery ◽  
Aymery Constant ◽  
Elise Bannier ◽  
...  
Keyword(s):  
Healthy Volunteers ◽  
Food Choice ◽  
Choice Task ◽  
Picture Database ◽  
Visual Cognitive

scholarly journals Effects of Imagery as Visual Stimuli on the Physiological and Emotional Responses

J ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 206-225 ◽  
Author(s):  
Nadeesha Gunaratne ◽  
Claudia Viejo ◽  
Thejani Gunaratne ◽  
Damir Torrico ◽  
Hollis Ashman ◽  
...  
Keyword(s):  
Heart Rate ◽  
Diastolic Pressure ◽  
Visual Stimuli ◽  
Systolic Pressure ◽  
Emotional Responses ◽  
Accurate Information ◽  
Dimensional Model ◽  
Picture Database ◽  
Face Scale ◽  
General Linear Regression Model

Study of emotions has gained interest in the field of sensory and consumer research. Accurate information can be obtained by studying physiological behavior along with self-reported-responses. The aim was to identify physiological and self-reported-responses towards visual stimuli and predict self-reported-responses using biometrics. Panelists (N = 63) were exposed to 12 images (ten from Geneva Affective PicturE Database (GAPED), two based on common fears) and a questionnaire (Face scale and EsSense). Emotions from facial expressions (FaceReaderTM), heart rate (HR), systolic pressure (SP), diastolic pressure (DP), and skin temperature (ST) were analyzed. Multiple regression analysis was used to predict self-reported-responses based on biometrics. Results showed that physiological along with self-reported responses were able to separate images based on cluster analysis as positive, neutral, or negative according to GAPED classification. Emotional terms with high or low valence were predicted by a general linear regression model using biometrics, while calm, which is in the center of emotion dimensional model, was not predicted. After separating images, positive and neutral categories could predict all emotional terms, while negative predicted Happy, Sad, and Scared. Heart Rate predicted emotions in positive (R2 = 0.52 for Scared) and neutral (R2 = 0.55 for Sad) categories while ST in positive images (R2 = 0.55 for Sad, R2 = 0.45 for Calm).

Development of a content-based-retrieval operative picture database system

1999 ◽  
Author(s):  
Shih-Tsang Tang ◽  
Ming L. Hsiao ◽  
Hsing-Yi Chen ◽  
Shuenn-Tsong Young
Keyword(s):  
Database System ◽  
Content Based Retrieval ◽  
Picture Database

scholarly journals Summarizing the image adjectives for the construction of the picture database for lifestyle image board

2018 ◽  
Vol 169 ◽  
pp. 01025
Author(s):  
Peng-Jyun Liu ◽  
Ming-Chuen Chuang
Keyword(s):  
Literature Review ◽  
Product Design ◽  
High Tech ◽  
Expert Interviews ◽  
Primary Stage ◽  
The Core ◽  
Picture Database

Design targets the promotion consumers’ motivation to buy more products, whose sensual appeal has become the core of design. This research targets at the lifestyles of three major groups of people in Taiwan: “high-tech groups”, “LOHAS groups”, and “quality groups”. Using literature review, questionnaires, and expert interviews, the frequently used images syntaxes used in three major dimensions: product design, designers, and lifestyle clusters are collected and summarized into 237 items. These image syntaxes are further categorized and selected, yielding 122 image syntaxes in six categories. At last, experts in different areas of design are requested to pick frequently used image syntaxes in the primary stage when designing for these three groups in order to construct the appropriate image syntaxes used in and their association with different areas of design and lifestyle clusters. The results can become the foundation of the next stage of this research in order to construct a lifestyle oriented image board database.

scholarly journals The Sexual OBjectification and EMotion database: A free stimulus set and norming data of sexually objectified and non-objectified female targets expressing multiple emotions

2021 ◽  
Author(s):  
Daniela Ruzzante ◽  
Bianca Monachesi ◽  
Noemi Orabona ◽  
Jeroen Vaes
Keyword(s):  
Sexual Objectification ◽  
Social Judgments ◽  
Basic Emotions ◽  
Future Studies ◽  
Picture Database ◽  
Neutral Face ◽  
Widespread Phenomenon ◽  
Per Se ◽  
Sexual Object ◽  
Internal And External Validity

AbstractSexual objectification – perceiving or treating a woman as a sexual object – is a widespread phenomenon. Studies on sexual objectification and its consequences have grown dramatically over the last decades covering multiple and diverse areas of research. However, research studying sexual objectification might have limited internal and external validity due to the lack of a controlled and standardized picture database. Moreover, there is a need to extend this research to other fields including the study of emotions. Therefore, in this paper we introduce the SOBEM Database, a free tool consisting of 280 high-resolution pictures depicting objectified and non-objectified female models expressing a neutral face and three different emotions (happiness, anger, and sadness) with different intensity. We report the validation of this dataset by analyzing results of 134 participants judging pictures on the six basic emotions and on a range of social judgments related to sexual objectification. Results showed how the SOBEM can constitute an appropriate instrument to study both sexual objectification per se and its relation with emotions. This database could therefore become an important instrument able to improve the experimental control in future studies on sexual objectification and to create new links with different fields of research.

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