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All Journal JOIV : International Journal on Informatics Visualization
Suryani, Indah Yudi
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Computer Science & IT

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Semantic Multi-Query Model for Cultural Computing of Image Search System Barakbah, Ali Ridho; Suryani, Indah Yudi; Kusumaningtyas, Entin Martiana
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4294

Abstract

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%.
Co-Authors Ali Ridho Barakbah Entin Martiana Kusumaningtyas