<![CDATA[The proliferation of digital images on the internet has increased the need for image search systems, especially for culturally significant images that contain a collection of impressions. However, traditional image search systems typically rely on a single query, making it difficult to discern user intent accurately. This paper introduces a novel model for describing user impressions using a semantic multi-query function for cultural computing in image search systems. This model provides a culture-centric semantic multi-image query system to generate representative query impressions. The proposed multi-query model provides an analytical tool to semantically construct representative query color attributes, involving four stages: (1) Local normalization of 3D-Color Vector Quantization, (2) Color distribution measurement, (3) Adaptive representative color adjustment, and (4) Representative color identification. For the experimental study, we evaluate our system with two types of experiments: (1) Multi-query image for image search to ensure that our multi-query model enhances the accuracy of the retrieval outcomes, and (2) Multi-query image for semantic image search of cultural paintings. In the first experiment using the SIMLIcity dataset, our proposed multi-query model achieved better retrieval performance across most categories, reducing the single-query error from 26.67% to 20%. In the second experiment using the Indonesian cultural painting dataset, our proposed multi-query model achieved better retrieval performance across most categories, improving the single-query average similarity from 46.6% to 72%.]]>
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