# Emotion-Based Classification and Indexing for Wallpaper and Textile
## 4 Experimentation
### Indexing Using Emotion and Complexity via Reference Image  
When the reference image is adopted for indexing, the reference image needs to be first analyzed and then the two features must be extracted to calculate the similarity between the reference image and the images in the database. This process is shown in Figures 18 and 19. The feature of \"emotion\" applies the Euclidean Distance to estimate the similarity between the reference image and the images in the database. The \"complexity\" feature uses the City Block Distance to estimate the similarity between the reference image and the images in the database. An example of indexing using the reference image is demonstrated in Figure 25, which shows the results for the same \"emotion\" in Figure 25b, for the same \"emotion\" and keeping the same \"complexity\" of \"dense\" in Figure 25c, and for the same \"complexity\" of \"dense\" but having different \"emotions\" in Figure 25d. Good results are shown in Figure 25 based on the proposed method. Figure 24: An example whereby the feature of complexity is added to the same emotion. Figure 25: _Cont._The outcome of a user study according to the emotions they feel from the images is shown in Table 1. Eighty people who are all Ethnic Chinese are selected to participate in and experience the system comprising three kinds of tests on indexing including \"Reference Image--Emotion and Complexity\"; \"Semantic--Emotion\"; and \"Semantic--Emotion and Complexity\". Every test in the system provides three options, which are the effects of human feeling including Satisfied, Partially satisfied, and Dissatisfied. In the test, every subject has to acquire 30 images from database using the system. When the number of the acquired images which match subject's feeling exceed 20, the subject is classified as Satisfied. When the number of the acquired images which match subject's feeling locate between 10 and 20, the subject is classified as partially satisfied. When the number of the acquired images which match subject's feeling are below 10, the subject is classified as Dissatisfied. The outcomes of the tests are shown in Table 1, indicating the proposed method is able to achieve good results. In particular, when the indexing adopts \"Emotion and Complexity\", we can gain a better outcome. However, since the emotions and messages concerning the content of an image are usually conveyed to humans, views differ because of differences in culture, race, gender or age. To obtain a good outcome, we need a good method and to choose suitable color-emotion models. Therefore, in the future, we suggest adopting different color-emotion models from different nationalities and cultures that have been researched and edited beforehand, to make them more widely adopted and universally applied. Figure 25: An example of indexing using a reference image. Table 2 demonstrates the correct rate of identification of the foreground and background based on the color complexity of images. Arbitrarily, 100 images that are used in each kind of color complexity were chosen for each kind of methods. It compares the proposed method and some previous methods, such as RPGB [43], KBC [41], and TMHG [44]. In these previous methods, the dominant color region in an image can be represented as a connected fragment of homogeneous color pixels which is perceived by human vision. The RPGB proposed a technique to index and store images based on dominant color regions; in which features like region size and the location of the region are extracted simply considering the color and spatial relations of the regions detected in the image. The main drawback of this technique is it never retrieves the same objects of varying sizes as the similar image. For the smaller object (flowers), the background will be the dominant region, as shown in Figure 26a, whereas, in bigger objects (flowers), the object itself is dominant, as shown in Figure 26b. Even though the semantics of the objects are the same, they are not retrieved as similar images.