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Inspiration-based Media Retrieval with Interactive Genetic Algorithm
computation has shown a great potential to work out several real-world
in the point of optimisation, but it is still quite far from realizing
of matching the human performance, especially in creative applications.
overcome this shortcoming, we present a promising technique called
genetic algorithm (IGA), which performs optimisation with human
the user can obtain what he has in mind through repeated interaction
show the usefulness of the IGA to develop effective inspiration-based
we have applied it to the problems of fashion design and emotion-based
Interactive Genetic Algorithm
First proposed by John Holland in 1975, genetic algorithm (GA) as one of the computational implementations is an attractive class of computational models that mimic natural evolution to solve problems in a wide variety of domains. However, most of the conventional applications of GA lack of the capability to utilize human intuition and emotion appropriately in creative applications such as architecture, art, music, and design. There is no clear measure to give the evaluation of fitness other than the one in the human mind.
Interactive GA (IGA) is a
technique that performs optimization with the human evaluation. A human
obtain what he has in mind through repeated interaction with the
the fitness function cannot be explicitly defined. This allows us to
effective human-oriented evolutionary systems, since this obtains from
the fitness value for the problem at hand, and produces better designs
images for the next generation.
Application to Fashion Design
Fig. 1 shows the fashion design aid system developed based on the IGA. There is a database of partial design elements, which are stored as 3D models. The system selects the models of each part and combines them into a number of individual designs. The population is displayed on screen and user gives fitness values to each design. Then, the system reproduces the population proportional to the fitness value of each design, and applies crossover and mutation to make the next generation. The results are displayed again in the screen with 3D graphics. Iteration of these processes can produce the population of higher fitness value, namely better designs.
Fig.1: Fashion design system using IGA
Application to Image Retrieval
The system is constructed as shown in Fig. 2. In the preprocessing step, at first, wavelet transform is performed for every image in the database and stored are the overall average color and the indices and signs of the m magnitude wavelet coefficients in a search table. The system displays twelve images, obtains the fitness values of the images from human, and selects candidates based on the fitness. Genetic operation, vertical or horizontal crossover, is applied to the selected candidates. To find the next twelve images, the stored image information is evaluated by each criterion. Twelve images of the higher magnitude value are provided as a result of the search.
Fig. 2: Image retrieval
system using IGA
presented an approach that implements inspiration-based media
systems with human preference and emotion using interactive genetic
Several experiments show that our approach allows to design and search
media not only explicitly expressed image, but also abstract images
“cheerful impression,” “gloomy impression,” and so on. It is expected
same approach can be applied to many problems in music retrieval and
manipulation based on intuition and inspiration.
Cho, S.-B., Lee, J.-Y. (2001). A human-oriented image retrieval system using interactive genetic algorithm, IEEE Trans. Systems, Man and Cybernetics-Part A. (in press)
Kim, H.-S., Cho, S.-B. (2000). Application of interactive genetic algorithm to fashion design, Engineering Applications of Artificial Intelligence, (13) 635-644.
Takagi, H. (1998). Interactive evolutionary computation: Cooperation of computational intelligence and human KANSEI, Proc. of Int. Conf. on Soft Computing, 41-50.