1 research outputs found
Semantic Diversity versus Visual Diversity in Visual Dictionaries
Visual dictionaries are a critical component for image
classification/retrieval systems based on the bag-of-visual-words (BoVW) model.
Dictionaries are usually learned without supervision from a training set of
images sampled from the collection of interest. However, for large,
general-purpose, dynamic image collections (e.g., the Web), obtaining a
representative sample in terms of semantic concepts is not straightforward. In
this paper, we evaluate the impact of semantics in the dictionary quality,
aiming at verifying the importance of semantic diversity in relation visual
diversity for visual dictionaries. In the experiments, we vary the amount of
classes used for creating the dictionary and then compute different BoVW
descriptors, using multiple codebook sizes and different coding and pooling
methods (standard BoVW and Fisher Vectors). Results for image classification
show that as visual dictionaries are based on low-level visual appearances,
visual diversity is more important than semantic diversity. Our conclusions
open the opportunity to alleviate the burden in generating visual dictionaries
as we need only a visually diverse set of images instead of the whole
collection to create a good dictionary