A method for extracting emotion using colors comprise the painting image

Paintings can evoke emotions in viewers. In this paper, we propose a method for extracting emotions from paintings by using the colors that comprise the paintings. The proposed approach is based on a color image scale, which is one of the popular experimental scales focusing on the relation between colors and emotions. We first construct a color combination and emotional word dataset. To this end, we create a color spectrum from the input painting. We then search for the best matching color combination from the dataset, which is most similar to the color spectrum. The best matching color combination is mapped to the corresponding emotional word. Afterward, we extract the emotional word as the emotion evoked by the painting. To evaluate the proposed method, we compared the results of the proposed algorithm to those of a user study on the extraction of emotions from several paintings. Through several experiments, we show that the proposed method exhibits excellent performance with respect to predicting the emotions evoked by a painting. Finally, we propose an image exploration system based on the emotion extraction method mentioned above. In this system, users can explore painting images emotionally coherently

Time-Efficient Hybrid Approach for Facial Expression Recognition

Facial expression recognition is an emerging research area for improving human and computer interaction. This research plays a significant role in the field of social communication, commercial enterprise, law enforcement, and other computer interactions. In this paper, we propose a time-efficient hybrid design for facial expression recognition, combining image pre-processing steps and different Convolutional Neural Network (CNN) structures providing better accuracy and greatly improved training time. We are predicting seven basic emotions of human faces: sadness, happiness, disgust, anger, fear, surprise and neutral. The model performs well regarding challenging facial expression recognition where the emotion expressed could be one of several due to their quite similar facial characteristics such as anger, disgust, and sadness. The experiment to test the model was conducted across multiple databases and different facial orientations, and to the best of our knowledge, the model provided an accuracy of about 89.58% for KDEF dataset, 100% accuracy for JAFFE dataset and 71.975% accuracy for combined (KDEF + JAFFE + SFEW) dataset across these different scenarios. Performance evaluation was done by cross-validation techniques to avoid bias towards a specific set of images from a database

A picture is worth a thousand words: The perplexing problem of indexing images

Indexing images has always been problematic due to their richness of content and innate subjectivity. Three traditional approaches to indexing images are described and analyzed. An introduction of the contemporary use of social tagging is presented along with its limitations. Traditional practices can continue to be used as a stand-alone solution, however deficiencies limit retrieval. A collaborative technique is supported by current research and a model created by the authors for its inception is explored. CONTENTdm® is used as an example to illustrate tools that can help facilitate this process. Another potential solution discussed is the expansion of algorithms used in computer extraction to include the input and influence of human indexer intelligence. Further research is recommended in each area to discern the most effective method

About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN