Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Fine-grained categorization is a challenging task mainly due to two factors: first, objects share similar appearances beten different categories; second, objects present significant pose variation within the same category. To address these challenges,  propose a method to automatically detect discriminative and pose-invariant regions, which is referred to as a polygon-based classifier (PBC). In the first stage,  generate a set of polygons that are composed of multiple parts. For each polygon, a classifier is trained based on deep features of a convolutional network. Then, a greedy algorithm is employed to select the discriminative and complementary polygon-based classifiers that deliver highest classification accuracy for fine-grained object categories. In the second stage, the confusing classes of the first stage are selected and employed to train the polygon-based classifiers. Then, a greedy algorithm is employed to select discriminative classifiers. For the test images,  use the classifiers trained in the first stage to obtain a coarse result. Then, the classifiers of the second stage are adopted to distinguish the confusing classes of the coarse result. In our experiments, the proposed approach is evaluated on three ll-known fine-grained datasets. The experiments show that our approach outperforms the state-of-the-art methods.
['Chao Huang', 'Hongliang Li', 'Yurui Xie', 'Qingbo Wu', 'Bing Luo']
I am writing a research paper on a idea PBC: Polygon-Based Classifier for Fine-Grained Categorization
[{"from": "human", "value": "I am writing a research paper on a idea PBC: Polygon-Based Classifier for Fine-Grained Categorization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Fine-grained categorization is a challenging task mainly due to two factors: first, objects share similar appearances beten different categories; second, objects present significant pose variation within the same category. To address these challenges,  propose a method to automatically detect discriminative and pose-invariant regions, which is referred to as a polygon-based classifier (PBC). In the first stage,  generate a set of polygons that are composed of multiple parts. For each polygon, a classifier is trained based on deep features of a convolutional network. Then, a greedy algorithm is employed to select the discriminative and complementary polygon-based classifiers that deliver highest classification accuracy for fine-grained object categories. In the second stage, the confusing classes of the first stage are selected and employed to train the polygon-based classifiers. Then, a greedy algorithm is employed to select discriminative classifiers. For the test images,  use the classifiers trained in the first stage to obtain a coarse result. Then, the classifiers of the second stage are adopted to distinguish the confusing classes of the coarse result. In our experiments, the proposed approach is evaluated on three ll-known fine-grained datasets. The experiments show that our approach outperforms the state-of-the-art methods."}]