Increasingly Specialized Perception Network for Fine-Grained Visual Categorization of Butterfly Specimens

Increasingly Specialized Perception Network for Fine-Grained Visual Categorization of Butterfly Specimens

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Existing fine-grained 2004 honda civic exhaust system categorization methods predominantly conquer challenges independently, while neglecting the fact that patch proposal and feature extraction can reinforce each other.This necessitates to extract the domain-specific representations and localize key (most discriminative) patches alternately, since implicit to fine-grained specialization is the existence of an entry-category visual shared among all categories.In this study, an increasingly specialized perception convolutional neural network (ISP-CNN) is proposed, focusing on a butterfly domain at sub-species level due to the biosystematics structure.Its pipeline is an coarse-to-fine specialization that hierarchically extracts fine-scale features and proposes distinctive patches at multiple scales.

Specifically, the framework consists of two highlights, i.e., hierarchical learning support vector machines (HL-SVMs) and patch proposal sub-networks (PPNs).Depending on the confidences obtained in HL-SVMs, the samples are classified at appropriate subset (i.

e., sub-family, genus, and sub-species).Then the PPNs zoom the images to shift the focus on the most representational patches by taking previous predictions of HL-SVMs as a reference, while a finer scale network takes as input an amplified attended region from previous scales with gradual steps.As for self-optimization, ISP-CNN is driven by a patch-level loss and a class-level loss, to mutually learn patch proposals and decisions.

For effectness verification, a total of 19,368 lab-made images of butterfly specimens spanning 48 tumbler skins sub-species are utilized as testing samples, while 116,208 augmented images are employed for training.ISP-CNN delivers the better or comparable performance, i.e., validation accuracy achieves 93.

67% and testing accuracy achieves 92.13%, which outperforms state-of-the-arts.