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This paper deals with image representation improvement using hybrid color spaces. This representation is important because it influences segmentation and classification results. We present two improvements of an existing supervised algorithm to obtain the most adapted hybrid color space for a given image. These improvements are based on a multi-objective optimization leading to a cost-efficiency trade-off, and have a theoretical justification. A comparison of the different approaches shows that the most adapted hybrid color space is reached with our algorithm and improves classification results.