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Bilinear pyramid network for flower species categorization

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Abstract

It is a challenging task to distinguish between numerous species of flowers due to their visually similarities and variations of the pose and structure. Thanks to properly modeling of the local feature interactions, bilinear CNN has succeeded in classifying of many non-rigid fine-grained species including flowers. However, bilinear CNN only computes the feature in a straightforward way without exploring the interactions between features from multiple layers in the network. In this paper, we present a novel Bilinear Pyramid Network (BPN) for flower categorization. Instead of passing through the network and directly feeding the final classifier, features from a convolutional layer are resized and multiplied with that from the former layer, which alternates multiple times to generates prediction vectors using the features from distinct layers. These features encoded from the feature pyramid spontaneously carry multi-level semantic cues, which yields stronger discriminative powers than single-layer features. Experiments show that the proposed network obtains superior classification results on the challenging dataset of flowers.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61962014, Grant 61702129, Grant 61772149, and Grant U1701267, in part by the China Postdoctoral Science Foundation under Grant 2018M633047, and in part by the Guangxi Science and Technology Project under Grant AB17195057 and Grant AA18118039.

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Authors and Affiliations

  1. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

    Cheng Pang, Wenhao Wang, Rushi Lan, Zhuo Shi & Xiaonan Luo

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  1. Cheng Pang
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  2. Wenhao Wang
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  3. Rushi Lan
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  4. Zhuo Shi
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  5. Xiaonan Luo
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Correspondence to Rushi Lan.

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Pang, C., Wang, W., Lan, R. et al. Bilinear pyramid network for flower species categorization. Multimed Tools Appl 80, 215–225 (2021). https://doi.org/10.1007/s11042-020-09679-8

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  • DOI: https://doi.org/10.1007/s11042-020-09679-8

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