Ranking-based triplet loss function with intra-class mean and variance for fine-grained classification tasks

Abstract

This paper proposed a deep ranking model for triplet selection to efficiently learn similarity metric from top ranked images. A modified distance criterion described in the current work leverages the intra-category variance in metric learning of a triplet network by learning a local sample structure. A multicolumn fusion architecture is used to capture different levels of variance, which when incorporated in the loss function strengthens it and optimizes the objective of the triplet networks. This enables a fine-grained classification strategy. State-of-the-art techniques use a group-sensitive triplet sampling to deal with this issue. However, these have the disadvantage of increased group sampling computations. Experiments are conducted over a variety of benchmark datasets including Model40, PatternNet, and In-Shop Clothing. The main purpose of these experiments are to verify whether the triplet learning technique can be applied over different kinds of data. Results demonstrate that the current work provides superior results in most cases. These results can further be improved with specific parameter tunings and ensembling techniques wherever applicable.