@Article{csse.2023.032047,
AUTHOR = {Vidit Kumar, Hemant Petwal, Ajay Krishan Gairola, Pareshwar Prasad Barmola},
TITLE = {Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {46},
YEAR = {2023},
NUMBER = {3},
PAGES = {2711--2724},
URL = {http://www.techscience.com/csse/v46n3/52226},
ISSN = {},
ABSTRACT = {Fine-grained image search is one of the most challenging tasks
in computer vision that aims to retrieve similar images at the fine-grained
level for a given query image. The key objective is to learn discriminative
fine-grained features by training deep models such that similar images are
clustered, and dissimilar images are separated in the low embedding space.
Previous works primarily focused on defining local structure loss functions
like triplet loss, pairwise loss, etc. However, training via these approaches
takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded space and
lose generalizability to unseen classes. This paper proposes a noise-assisted
representation learning method for fine-grained image retrieval to mitigate
these issues. In the proposed work, class manifold learning is performed
in which positive pairs are created with noise insertion operation instead
of tightening class clusters. And other instances are treated as negatives
within the same cluster. Then a loss function is defined to penalize when
the distance between instances of the same class becomes too small relative
to the noise pair in that class in embedded space. The proposed approach is
validated on CARS-196 and CUB-200 datasets and achieved better retrieval
results (85.38% recall@1 for CARS-196% and 70.13% recall@1 for CUB-200)
compared to other existing methods.},
DOI = {10.32604/csse.2023.032047}
}