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Weak supervision fine-grained image classification algorithm based on correlation-guided discriminant learning

A classification algorithm and correlation technology, applied in the field of computer vision, can solve problems such as ignoring internal spatial correlation

Inactive Publication Date: 2020-02-14
DALIAN UNIV OF TECH
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Problems solved by technology

However, these methods ignore the internal spatial correlation

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  • Weak supervision fine-grained image classification algorithm based on correlation-guided discriminant learning
  • Weak supervision fine-grained image classification algorithm based on correlation-guided discriminant learning
  • Weak supervision fine-grained image classification algorithm based on correlation-guided discriminant learning

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[0071] In order to make the purpose, technical solution and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below.

[0072] Datasets: Experimental evaluations are conducted on two benchmark datasets: Caltech-UCSD Birds-200-2011 and Stanford Cars, which are widely used competition datasets for fine-grained image classification. The CUB-200-2011 dataset covers 200 bird species and contains 11788 bird images, which are divided into a training set of 5994 images and a test set of 5794 images. The Stanford car dataset contains 16,185 images of 196 categories with about 50 groupings in each category.

[0073] Implementation Details: In all our experiments, all images are resized to 448×448. We use the fully convolutional network ResNet-50 as the feature extractor and apply "batch normalization" as the regularizer. Our optimizer uses Momentum SGD with an initial learning rate of 0.001 and ...

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Abstract

The invention belongs to the technical field of computer vision, and provides a weak supervision fine-grained image classification algorithm based on correlation-guided discriminant learning. An end-to-end correlation guided discriminant learning model is provided to fully mine and utilize the correlation of weak supervision fine-grained image classification to improve discrimination. First, a discriminative region packet sub-network is proposed, which first establishes correlations between regions and then enhances each region by weighted summary of all correlations from other regions to guide the network to discover more discriminative region groups. And finally, proposing a discriminative feature enhancement sub-network to mine and learn the internal spatial correlation between the feature vector elements of each patch, and improving the local discrimination capability of the patch by jointly enhancing the information elements and suppressing useless elements at the same time. A large number of experiments prove the effectiveness of DRG and DFS, and the most advanced performance is achieved.

Description

technical field [0001] The invention belongs to the technical field of computer vision, starts from improving the accuracy and efficiency of fine-grained image classification, and proposes a weakly supervised fine-grained image classification algorithm based on correlation-guided discrimination learning. Background technique [0002] Different from general image classification, weakly supervised fine-grained image classification (WFGIC) only uses image-level labels to recognize objects under more detailed categories and granularity. Due to its large number of potential applications in image understanding and computer vision systems, WFGIC has attracted extensive attention from academia and industry. WFGIC is an open problem in computer vision for two reasons. First, images belonging to the same subcategory have large differences in size, pose, color, and background, while images in different subcategories may be very similar in these aspects. Second, in addition to object ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/217G06F18/24
Inventor 王智慧王世杰李豪杰唐涛
Owner DALIAN UNIV OF TECH
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