Eun-Jong Park

Emotional Evaluation of Color Patterns Based on Rough Sets

If the emotion that a man or woman feels seeing color patterns in average sense can be extracted as rules, the result is useful to make an emotion-based color image retrieval system. This paper shows that the rough set theory provides a convenient tool for the purpose. We collect the emotion data when people see a set of predesigned random color patterns and extract the coarse rules for the emotional evaluation of the color patterns using VPRS (Variable Precision Rough Set) theory. Those rules can be used not only to approximately evaluate color patterns such as wall papers but also to set the initial conditions for the precise mapping system based on adaptive fuzzy logic from image features to emotion spaces represented by linguistic image scales.

Fuzzy Similarity-Based Emotional Classification of Color Images

This paper proposes a novel scheme for evaluating an emotional response to color images. The proposed scheme uses case-based reasoning in which the prototypical color images for each emotion are stored as cases and are compared with the images to be evaluated. In the comparison, the similarities in terms of image descriptors play an important role, and their combination is crucial for the construction of a proper similarity measure. In the training phase of the proposed scheme, the weights that represent the unequal importance of each descriptor is determined in order to obtain a similarity measure that can be used to evaluate and classify a color image with respect to a pair of emotions. Prior to classification, the representative color images are chosen for each emotion by human subjects and are stored as cases. The stored images are compared with an image to be classified using the constructed similarity measure to determine which emotion is appropriate between a pair of emotions. In this study, we used color and texture descriptors recommended by MPEG-7, represented as high-dimensional vectors. In the training, we proposed a method based on the rough approximation and the fuzzy inter- and intra-similarities to determine the weights that represent the unequal importance of the complex MPEG-7 descriptors. Experimental results show a promising performance for the proposed scheme, and better performance could be achieved by including more prototypical images as cases.

Vision-based self-localization of autonomous guided vehicle using landmarks of colored pentagons

This paper describes an idea for determining self-organization using visual land marks. The critical geometric dimensions of a pentagon are used here to locate the relative position of the mobile robot with respect to the pattern. This method has the advantages of simplicity and flexibility. This pentagon is also provided with a unique identification, using invariant features and colors that enable the system to find the absolute location of the patterns. This algorithm determines both the correspondence between observed landmarks and a stored sequence, computes the absolute location of the observer using those correspondences, and calculates relative position from a pentagon using its five vertices. The algorithm has been implemented and tested. In several trials it computes location accurate to within 5.4 centimeters in less than 0.3 second.

Interactive color planning system based on MPEG-7 visual descriptors

In this paper, an interactive color planning system is proposed. The core of the system is an emotion-based pattern retrieval that can be implemented with two data bases. In the system a user can issue an adjective word that represents the desired feeling to the system. Then the system retrieves the color combinations or representative color patterns from the knowledge base. Again when the user selects a color combination or representative color pattern, the system provides similar product patterns from the product DB according to MPEG-7 visual descriptors. Finally the user can choose the color patterns and apply them to the 3D VRML space. The decision support system can help users who do not have enough knowledge about color planning and can be used to promote e-business.

On-line Background Extraction in Video Image Using Vector Median

Background extraction is an important technique to find the moving objects in video surveillance system. This paper proposes a new on-line background extraction method for color video using vector order statistics. In the proposed method, using the fact that background occurs more frequently than objects, the vector median of color pixels in consecutive frames Is treated as background at the position. Also, the objects of current frame are consisted of the set of pixels whose distance from background pixel is larger than threshold. In the paper, the proposed method is compared with the on-line multiple background extraction based on Gaussian mixture model(GMM) in order to evaluate the performance. As the result, its performance is similar or superior to the method based on GMM.