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Image classification method of plankton based on multi-feature fusion convolutional neural network

A convolutional neural network and multi-feature fusion technology, applied in the field of plankton image classification, can solve problems such as difficulty in classifying plankton images, and achieve the effect of improving the accuracy rate

Active Publication Date: 2020-03-13
OCEAN UNIV OF CHINA
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Problems solved by technology

[0003] This application solves the technical problem of difficult classification of large-scale multi-category plankton images in the prior art by providing a plankton image classification method based on multi-feature fusion convolutional neural network

Method used

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  • Image classification method of plankton based on multi-feature fusion convolutional neural network
  • Image classification method of plankton based on multi-feature fusion convolutional neural network

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Embodiment

[0029] A plankton image classification method based on multi-feature fusion convolutional neural network, such as figure 1 shown, including the following steps:

[0030] S1: Collect clear plankton images and build a large-scale multi-category plankton image data set. The plankton images in this data set are used as original feature images. The number of collected images is about 30,000 to 90,000, and the types of plankton are About 30 to 50 categories;

[0031] S2: Process the original feature image, extract the global feature of plankton, and obtain the global feature image. The specific processing steps are:

[0032] S21: Using the image segmentation Scharr operator to convert the original feature image, the converted image includes global features and local features;

[0033] S22: Using a bilateral filtering method to remove local features in the converted image;

[0034] S23: Enhance the contrast to highlight the global features in the converted image;

[0035] S3: Pro...

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Abstract

The invention provides a plankton image classification method based on multi-feature fusion convolutional neural network. First, a large number of clear plankton images are collected, and a large-scale multi-category plankton image data set is constructed, and then image transformation and edge extraction algorithms are used. , extract the global features and local features, put the original feature image, the global feature image and the local feature image into the multi-feature fusion convolutional neural network of deep learning for training, and obtain the multi-feature fusion convolutional neural network model. Finally, the The plankton image is input into the multi-feature fusion convolutional neural network model, and the classification can be realized according to the final output probability score. The invention combines biomorphological angles, computer vision methods and deep learning technology, and has very high classification accuracy especially for large-scale multi-category plankton images.

Description

technical field [0001] The invention relates to the technical fields of biomorphological analysis, computer vision and deep learning, in particular to a plankton image classification method based on a multi-feature fusion convolutional neural network. Background technique [0002] Due to the importance of plankton in ecosystems, the processing and analysis of plankton images is becoming more and more important. However, due to the large number of types of plankton, and there are also very large differences in morphological characteristics and other aspects of various types of plankton. For plankton images, the appearance of the same type of plankton is not necessarily identical, and may have great differences. For different types of plankton, their appearance and other characteristics may also have a high degree of similarity. This intra-category difference and inter-category similarity pose a huge challenge to plankton image classification. Traditional image classificatio...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/46G06K9/62G06T7/00G06T7/41
CPCG06T2207/20081G06T2207/20084G06V10/443G06V10/462G06F18/2415
Inventor 郑海永王超俞智斌戴嘉伦郑冰
Owner OCEAN UNIV OF CHINA
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