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Intention-oriented computational visual attention (ICVA) model attempts to imitate human vision by computational intelligence. This paper contributes to enabling the ICVA model with learning ability so as to acquire or change intention according to assigned image samples. This innovative design is called the self-learning ICVA model which contains a neuro-fuzzy network to learn intention from image samples. A well-trained self-learning ICVA model can find interested objects in images by extracting attentive areas and matching them with intention expressed by fuzzy rules. By extracting fuzzy rules from image samples, the self-learning ICVA model acquires or changes the intention. The whole design is verified by constructing an intelligent road sign detection system. Experimental results show the system succeeds in learning and seeking image content with rectangular road signs.