Culture-Dependent Features Computation Model

for Emotion-based Image Retrieval

 

Student Name: Totok Suhardijanto (Doctor Student 2nd Year)

Supervisor: Prof. Kiyoki Yasushi

Student ID: 80849341

 

 

Abstract

 

In this research, we developed a model for computing cultural contents in an image retrieval system. The key point in the model is the mechanism in providing the more accurate information about relationship between color and cultural related emotion aspects in two cultures or more (C1, C2 c Cn). Generally speaking, in the research area of information retrieval, it is still difficult to consider cultural related features into an image search system. Current image retrieval systems are developed based on low-level features such as texture, shape, color and position. High-level features such as face recognition, emotions and culture-differences still remain difficult to be used in more open-ended task of image retrieval systems.

In this research, we designed an image retrieval system for a specific domain that is dealing with color and emotion related features. We implemented a method that allows an information retrieval system to deal with image based on culture-dependent color-emotion association. A vector space model was implemented in this system to create a culture oriented computation that is more feasible to deal with culture related information across cultures. In order to realizing this system, image color features and a given culture related color-emotion characteristics converted into vectored numeric data, then stored as metadata. By using similarity and weighting computation, the system provides dynamic information of the association between image color and cultural emotion.  

           

Background

 

In recent days, the computer research community began to consider cultural contents into their research [12]. One of the fields that attract more attention for the research community is image retrieval research. Nowadays, there are large quantities of images and visual information available. These images are existing in structured collections (e.g. museum collections) or independent (e.g. images found in Web pages in the form of individualsf photographs, logos, and so on) [4]. Collection of images became vary not only in number, but also in types because nowadays people with different cultural backgrounds enable to share their images over the world. Along with the trend of cross-cultural communication issues, an image retrieval system that enables to take culture-dependent issue into consider has more challenges in near future to provide better ways and approaches in dealing with culture-dependent images.

With regard to culture-dependent image retrieval, the issue of association between image color and emotions or impression is essential. This issue has already been addressed for many years. It has been attracting many scholars from various areas of studies. The color emotion oriented image retrieval system has been already proposed in number of researches [5], [6], [10], [11]. Current computer vision techniques allow us to extract automatically low-level features of images, such as color, texture, shape and spatial location of image elements, but it is difficult to extract high level features automatically, such as names of objects, scenes, behaviors and emotions [7]. Current image retrieval still has semantic gap in dealing with cross cultural environments [2], [3]. Although several emotion based image retrieval (EBIR) systems and emotion semantic image research (ESIR) systems have already been proposed in [7], [10], [11], these systems have not addressed issues yet for culture-dependent emotion. In our approaches, we consider cultural features of color and emotion association as one of key point for assessing cultural differences in image retrieval system. Figures 1 shows the difference between the previous model and our model.

 

EBIR/ESIR (Emotion-Based Image Retrieval/Emotion-Semantic Image Retrieval) System Overview

 

 

 

 

 

 

 

Our Culture-Dependent Emotion-Based Image Retrieval (CD-EBIR) System Overview

 

 

 

 

 

 

 

 

 

 

 

Figure 2: The Architecture Comparison between ESIR/EBIR and our CD-EBIR

 

 

Objective

In this research we developed a culture-dependent color emotion model for cross-culture oriented image retrieval system that realizes color emotion spaces to search images with human emotion aspects. Our system created color impression spaces based on Plutchick Model of emotion [8], [9]. We created our method based on the theory of color psychology that approaches color and emotion association according to basic features of emotion and color.

With regard to the multicultural reality of our world, there are also always slight differences of color and emotion association across cultures. This approach makes our method to be more similar to cross-cultural interaction in natural way. In the natural cross-cultural interaction, there is always a slight difference in color and emotion association among cultures. A color that means bravery in one culture could have different impression in another culture. Figure 2 illustrates the core system in the proposed model.

 

 

 

 

 

 

 

 

 

 

Figure 2: Core System (Culture-Dependent Emotion-Based Image Retrieval (CD-EBIR) Model)

 

We generated culture-dependent emotion based image metadata through these following steps. First, color features are extracted using 3-D Color Vector Quantization of RGB color space [1]. In this step, color features are converted into RGB color space. Separately, culture-dependent emotion features are acquired from cultural knowledge through a survey that is addressed to respondents from three different cultures including Vietnamese, Indonesian and Japanese. Respondents were asked to indicate their emotion or impression based on Plutchik model of emotion [8], [9] that associates to certain colors. This survey result is used for creating culture-dependent color semantic metadata.

            After the culture-dependent emotion information is obtained, this emotion feature is then represented as a vector to make it possible to map onto the RGB color space to correlate with nearest colors. Because a particular color can associate with different emotions and vice versa, in order to generate dynamically representative colors of emotion, we use automatic clustering method. After representative colors are chosen and irrelevant colors are excluded, we used them in creating culture-dependent color emotion metadata. In our culture-dependent emotion-based image retrieval, the query emotion is processed and mapped onto the color-emotion space. We implemented feature weighting and merging processing in order to measure the similarity for selecting the result. Figure 3 shows the system architecture of the proposed system.

 

Figure 3 The Architecture of CD-EBIR System

 

In order to obtain culture information of color and emotion, we designed and distributed survey to subjects in three different cultures including Japanese, Vietnamese, and Indonesian. According to this information, we created metadata repository of culture-oriented emotion features. Separately we developed a culture-oriented image feature metadata by extracting color features from a cultural image such as painting. Based on culture-oriented emotion and color feature metadata, we created a color-emotion space. We map color and emotion features onto space to make it possible to measure closest distance between the features based on the similarity and weight computation. A culture-dependent emotion-color subspace is implemented to reduce the result lists and to select a particular culture-oriented features that are closely related to the query. An automatic merging system is applied to integrate the result lists of collection. This step also involves performance analysis of the experimental results to measure performance and quality of the system. We implemented all the components of our technique and evaluated the prototype in a small scale dataset. The experimental dataset consists of 1000 images.

 

 

Result

 

Our culture-dependent emotion-based image retrieval system shows an ability to deal with image search in three different cultures. This system includes three core components, that is, color and emotion-space, culture-specific subspace, and automatic weighting function. In the color-emotion space, the query is processed by analyzing its emotion-related features and computing it through automatic feature weighting to correlate them with the correspondent color features. The information of correlation between emotion and color features are useful to retrieve a set of images that match to the query. A culture specific subspace which is implemented is also useful to select a culture that is targeted by the query. The output of this system is provided into lists of images that are related to a specific culture.

For the experimental studies, we implemented our system to image datasets containing Japanese and Indonesian painting images. We designed a database of Japanese Ukiyo-e based on Tokyo National Gallery Database and Indonesian Balinese paintings. The experimental results show that our system can select and find out a set of culture-oriented emotion-based images by submitting query with emotion and impression keywords.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3 Our Web-based Cultural-Dependent Image Search System

 

 

In comparison to human impression based selecting result, the precision rate in our system is still under 70%. It is still difficult to distinguish colors based on a set of closest emotion and impression such as joy, contentment, and satisfaction. However, our system can indicate a set of emotion related colors in specific culture. For instance, Japanese and Vietnamese happiness relate to a set of colors between red and blue, whereas Indonesian happiness refers to a set of colors between yellow and green.

 

 

References

 

[1]   Barakbah, A.R. and Y. Kiyoki. 3D-Color Vector Quantization for Image Retrieval Systems, International Database Forum (iDB), Iizaka, Japan, 2008.

[2]  Semeulders, A.W.M. Content-Based image retrieval at the end of early years. IEEE Trans. On Pattern Analysis and Machine Intelligence  22, 12, 2000, pp. 1349-1380

[3]  Zhao, R., William, I. and Grosky. Bridging the semantic gap in image retrieval. Distributed multimedia databases: techniques & applications. Idea Group Publishing Hershey PA USA, 2002, 14-36.

[4]   Kherfi, M.L., Ziou, D. and Bernardi, A. Image Retrieval From the World Wide Web: Issues, Techniques, and Systems. ACM Computing Surveys, Vol. 36, No. 1, March 2004, pp. 35–67.

[5]   Sasaki, S., Itabashi, Y., Kiyoki, Y. and Chen, X. An Image-Query Creation Method for Representing Impression by Color-based Combination of Multiple Images. Frontiers in Artificial Intelligence and Applications; Vol. 190, Proceeding of the 2009 Conference on Information Modelling and Knowledge Bases XX, 2009.

[6]   Wang, S. and Wang, X. Emotion Semantics Image Retrieval: A Brief Overview. ACII 2005, LCNS 3784, 2005, pp. 490-497. 

[7]   Wang, W.N. and Yu, Y.L. Image emotional semantic query based on color semantic description. Proceedings of ICMLC 2005, 2005, pp. 4571-4576. 

[8]   Plutchik, R. Emotion: Psychoevolutionary Synthesis. New York: Harper, 1980.

[9]   Plutchik, R. Emotions and Life: Perspective from Psychology, Biology, and Evolution. Washington D.C.: American Psychology Associations, 2003. 

[10] Wang, W.N and He, Q.H. A Survey on Emotional Image Retrieval. Proceeding of ICIP 2008, 2008, pp. 117-120.

[11] Solli, M. and Lenz, R. Color Based Bags-of-Emotions. CAIP 2009, LCNS 5702, 2009, pp. 573-580.

[12] Tosa, N., Matsuoka, S., Ellis, B., Ueda, H. and Nakatsu, R. eCultural computing with context aware application: ZENetic computerf, Lecture Notes in Computer Science, Vol. 3711, pp.13–23, 2010.