# Emotion-Based Classification and Indexing for Wallpaper and Textile
## 2 Related Works  
Human emotion has been studied and applied to areas such as color-emotion transference [3; 4; 5; 6; 7], emotion recognition interfaces [1; 2; 3; 4; 5; 6; 7], color psychological study [8; 9; 10; 11; 12; 13; 14; 15] and content-based retrieval systems [24; 25; 26]. An automatic color-emotion transferring algorithm was first proposed by [PERSON] et al. [16]. This shifts and scales the pixel values of the input image to match the mean and standard deviation of the reference image. Although the [PERSON] study was performed in 2001, psychological experiments were performed as early as 1894, when [PERSON] proposed the first empirical method for color preference [27; 28]. Despite the considerable number of studies, [PERSON] [29] reported the findings of these early studies were diverse and tentative. Later, [PERSON] [10] developed a color-emotion model that obtained data in psychological studies to identify emotional features in the field of fashion. In the Complete Guide to Colour Psychology [30], two color-emotion models were proposed: one for a single color and one for a combination of colors. The two main categories in the study of single color-emotion are classification and quantification. The classification of a single color-emotion uses principal component analysis to reduce a large number of colors to a small number of categories [31]. Figure 3: The schematic diagram for this method. Early studies to quantify color-emotion were undertaken by [PERSON] and [PERSON] [32], who provided a color-emotion model to quantify three color-emotion factors: activity, weight and heat. This study was then verified by [PERSON] et al. [19]. Studies of color combinations by [PERSON] [9] and [PERSON] et al. [17; 18] showed a simple relationship between emotion and color, and between an emotion and a color pair. [PERSON] et al. [33] used set theory to evaluate color combinations. Whereas studies of color-emotion only considered color pairs, the proposed approach uses a color-emotion scheme called the Color Scheme Bible Compact Edition [14], utilizing a predefined color-emotion guide. Compared to a single or two color-emotion, color combinations provide a simpler and more accurate description of color-emotion in the images. [PERSON] [34] reported emotion semantics are the most abstract semantic structure in images because they are closely related to cognitive models, cultural background and the aesthetic standards of users. Of all of the factors affecting the emotion of images, colors are considered the most important. [PERSON] found the relative importance of each heterogeneous feature of attractiveness is color > size > spatial frequency and the relative contribution of each heterogeneous feature to attractiveness is color heterogeneity > size heterogeneity > shape heterogeneity > texture heterogeneity. Two methods to extract emotion features from images were soon developed. First, a domain-based method was developed to extract relevant features, based on specialist knowledge of the application field [19]. For instance, [PERSON] [35] formulated a theory for the use of color and color combinations in art and included the semantics. The other method to extract emotional features is to perform psychological experiments to identify the types of features significant affecting users. To this effect, [PERSON] [36] proved the fractal dimensions of the images are related to the specific properties of an image. For textiles, [PERSON] et al. [2] presented an approach using physical feature extraction and fuzzy-rule-based evaluation to label images, using human emotion or feelings, which demonstrated positive results. They then proposed a method using a neural network for emotion-based textile indexing. The [PERSON] et al. proposed indexing system was tested using 160 textile images. For each textile image, seventy people manually annotated the emotions that the images inspired. However, this experiment analyzed the opinions of only a few people.