| Abstract |
Assessing visual similarity in-the-wild, a core ability of the human visual system, is a challenging problem for computer vision methods because of its subjective nature and limited annotated datasets. We make a stride forward, showing that visual similarity can be better studied by isolating its components. We identify color composition similarity as an important aspect and study its interaction with
category-level similarity. Color composition similarity considers the distribution of colors and their layout in images.
We create predictive models accounting for the global similarity that is beyond pixel-based and patch-based, or histogram level information. Using an active learning approach, we build a large-scale color composition similarity dataset with subjective ratings via crowd-sourcing, the first of its kind. We train a Siamese network using the dataset to create a color similarity metric and descriptors which outperform existing color descriptors. We also provide a benchmark for global color descriptors for perceptual color similarity. Finally, we combine color similarity and category level features for fine-grained visual similarity. Our proposed model surpasses the state-of-the-art performance
while using three orders of magnitude less training data.
The results suggest that our proposal to study visual similarity
by isolating its components, modeling and combining
them is a promising paradigm for further development. |