Deep color semantics for E-commerce content-based image retrieval
Pakizar Shamoi, Atsushi Inoue, Hiroharu Kawanaka · International Joint Conference on Artificial Intelligence · 2015
This paper aims to develop a methodology to retrieve images based on fuzzy dominant colors expressed through linguistic descriptions. This process involves two steps: assigning fuzzy colorimetric profile to the image and processing the user query. People regard color as an aesthetic issue, especially when it comes to choosing the colors for their clothing, apartment design and other objects around. It is often quite difficult to label these colors exactly using finite set of categories. Fuzzy color model that we are proposing represents the collection of fuzzy sets providing the conceptual quantization of crisp HSI space having soft boundaries. Most online shops tend to use conventional tag-based image retrieval systems. Making use of color visual content is still not disseminated in e-commerce. Subjectivity and sensitivity of humans in color perception and bridging the semantic gap between low-level visual features and high-level concepts are major issues that we plan to tackle in this research.