CN106372648A - Multi-feature-fusion-convolutional-neural-network-based plankton image classification method - Google Patents

Multi-feature-fusion-convolutional-neural-network-based plankton image classification method Download PDF

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CN106372648A
CN106372648A CN201610912684.1A CN201610912684A CN106372648A CN 106372648 A CN106372648 A CN 106372648A CN 201610912684 A CN201610912684 A CN 201610912684A CN 106372648 A CN106372648 A CN 106372648A
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plankton
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CN106372648B (en
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郑海永
王超
俞智斌
戴嘉伦
郑冰
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Ocean University of China
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    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
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    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
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Abstract

The invention provides a multi-feature-fusion-convolutional-neural-network-based plankton image classification method. The method comprises: lots of clear plankton images are collected and a large-scale multi-type plankton image data set is constructed; a global feature and a local feature are extracted by using an image conversion and edge extraction algorithm; an original feature image, a global feature image, and a local feature image are inputted into a depth-learning multi-feature-fusion convolutional neural network to carry out training, thereby obtaining a multi-feature-fusion convolutional neural network model; and then the plankton images are inputted into the multi-feature-fusion convolutional neural network model and classification is carried out based on a finally outputted probability score. According to the invention, the angle of biological morphology, the computer vision method, and the depth learning technology are combined; and thus the classification accuracy for plankton images, especially large-scale multi-type plankton images is high.

Description

Plankton image classification method based on multiple features fusion convolutional neural networks
Technical field
The present invention relates to biological morphology analysis, computer vision and deep learning technology field are and in particular to a kind of base Plankton image classification method in multiple features fusion convolutional neural networks.
Background technology
Due to importance in ecosystem for the plankton, the process of plankton image and analysis become increasingly to weigh Will.However, because planktonic number of species is huge, and the plankton of various species there is also at aspects such as morphological characteristics Very big difference.For plankton image, same kind of plankton, its profile is not necessarily identical, may There is very big difference, and for different types of plankton, the feature such as its profile is likely to there is high similarity.This Similarity between diversity and class in class, brings huge difficult problem to plankton image classification.Traditional image classification method is main The method being combined using the design of feature extraction and classifying device, but common feature extracting method is not particularly suited for swimming of complexity Biometric image, and special feature extracting method needs to take a substantial amount of time to carry out research design with energy, and advise for big The classification of the multi-class plankton image of mould can not obtain good result.
Content of the invention
The application passes through to provide a kind of plankton image classification method based on multiple features fusion convolutional neural networks, with Solve the difficult technical problem of multi-class on a large scale plankton image classification in prior art.
For solving above-mentioned technical problem, the application employs the following technical solutions and is achieved:
A kind of plankton image classification method based on multiple features fusion convolutional neural networks, comprises the steps:
S1: collection clearly plankton image, builds plankton image data set multi-class on a large scale, wherein, Plankton image in this data set is as primitive character image；
S2: process primitive character image, extract planktonic global characteristics, obtain global characteristics image, concrete process Step is:
S21: using image segmentation scharr operator, primitive character image is changed, the image after conversion includes the overall situation Feature and local feature；
S22: remove the local feature in transition diagram picture using bilateral filtering method；
S23: enhancing contrast ratio is projecting the global characteristics in transition diagram picture；
S3: by the canny edge detection algorithm of computer vision, primitive character image is processed, extract life of swimming The Edge texture feature of thing, i.e. planktonic local feature, obtain local feature image；
S4: build the multiple features fusion convolutional neural networks model based on primitive character, global characteristics and local feature, should Multiple features fusion convolutional neural networks include three separate basic sub-networks, and each basic sub-network is respectively trained original Characteristic image, global characteristics image and local feature image, wherein, 1 to 5 layers of this multiple features fusion convolutional neural networks is volume Lamination, 6 to 8 layers is full articulamentum；
S5: whole primitive character images, global characteristics image and the local that step s1, step s2 and step s3 are obtained Characteristic image is input in this multiple features fusion convolutional neural networks model and is trained, and finally gives the multiple features after optimization and melts Conjunction convolutional neural networks model:
S51: initial state information is set first, including iterationses, learning rate and initialization mode；
S52: this multiple features fusion convolutional neural networks model is carried out with fl transmission and feeds back with backward, make this multiple features melt Close convolutional neural networks model to be trained according to the plankton image of input and learn；
S53: output loss function value and accuracy rate；
S54: lift the performance of this multiple features fusion convolutional neural networks model by reducing loss function value；
S54: judge whether to reach the iterationses of setting, if it is, training finishes, the multiple features after being optimized melt Close convolutional neural networks model, otherwise, continue to redirect execution step s52；
S6: plankton image to be sorted is input in the multiple features fusion convolutional neural networks model after optimization, According to the probability score of final output, judge the classification corresponding to plankton image.
Further, according to practical situation and demand, described basis sub-network can using alexnet, vggnet or Any one convolutional neural networks in googlenet.Accurate based on the final classification of multiple features fusion convolutional neural networks model Really rate can step up according to the difference of selected basis sub-network, and correspondingly, the time cost of model training also can be progressively Increase.
It is that multiple Feature Mapping figures are directly merged mostly in prior art, in order that three kinds of features preferably merge, fill Divide and excavate with high-dimensional and level information, as a kind of preferred technical scheme, handed over using full connection in described full articulamentum Fork mixed method merges the Feature Mapping figure that three sub- network trainings obtain.
Differ greatly from view of between global characteristics image and local feature image, this full connection mixing together side Method, compared with commonly full articulamentum directly merges, effectively reduces global characteristics image and local feature image co-registration is brought Error, realizes the abundant fusion of multiple features, lifts plankton image classification accuracy rate.
Compared with prior art, the technical scheme that the application provides, the technique effect having or advantage are: from biomorph Learn angle to set out with computer vision methods, combine with deep learning technology, realize plankton figure multi-class on a large scale The classification of picture, drastically increases the accuracy rate of classification.
Brief description
Fig. 1 is method of the present invention flow chart；
Fig. 2 is the multiple features fusion convolutional neural networks illustraton of model of the present invention.
Specific embodiment
The embodiment of the present application is passed through to provide a kind of plankton image classification based on multiple features fusion convolutional neural networks Method, to solve the technical problem of multi-class on a large scale plankton image classification hardly possible in prior art.
In order to be better understood from technique scheme, below in conjunction with Figure of description and specific embodiment, right Technique scheme is described in detail.
Embodiment
A kind of plankton image classification method based on multiple features fusion convolutional neural networks, as shown in figure 1, include as Lower step:
S1: collection clearly plankton image, builds plankton image data set multi-class on a large scale, wherein, , as primitive character image, the amount of images of collection is about 30,000 to 90,000 for plankton image in this data set , planktonic classification is about in 30 to 50 classes；
S2: process primitive character image, extract planktonic global characteristics, obtain global characteristics image, concrete process Step is:
S21: using image segmentation scharr operator, primitive character image is changed, the image after conversion includes the overall situation Feature and local feature；
S22: remove the local feature in transition diagram picture using bilateral filtering method；
S23: enhancing contrast ratio is projecting the global characteristics in transition diagram picture；
S3: by the canny edge detection algorithm of computer vision, primitive character image is processed, extract life of swimming The Edge texture feature of thing, i.e. planktonic local feature, obtain local feature image；
S4: build the multiple features fusion convolutional neural networks model based on primitive character, global characteristics and local feature, should Multiple features fusion convolutional neural networks include three separate basic sub-networks, and each basic sub-network is respectively trained original Characteristic image, global characteristics image and local feature image, according to practical situation and demand, described basis sub-network can use Any one convolutional neural networks in alexnet, vggnet or googlenet, based on multiple features fusion convolutional Neural net The final classification accuracy of network model can step up according to the difference of selected basis sub-network, correspondingly, model training Time cost also can be stepped up, in the present embodiment, basic sub-network employs alexnet convolutional neural networks, wherein, 1 to 5 layers of this multiple features fusion convolutional neural networks are convolutional layer, and 6 to 8 layers is full articulamentum；
As shown in Fig. 2 each basic sub-network is separate in this model, mutually incoherent, each basic sub-network is One convolutional neural networks, and structure configuration is identical: each basic sub-network comprises 5 convolutional layers, and each convolutional layer Convolution kernel taper into, convolution kernel increasing number, each layer of convolution kernel size is respectively 11x11,11x11,5x5,3x3 and 3x3, and convolution kernel quantitative classification is 96,96,384,384 and 256.
It is that multiple Feature Mapping figures are directly merged mostly in prior art, in the present invention, in order that three kinds of features are more Merge well, fully excavate with high-dimensional and level information, as a kind of preferred technical scheme, in three basic sub-networks Afterwards, be a full articulamentum mixing together, in described full articulamentum, three bases are merged using the full cross-mixing method that connects The plinth sub-network Feature Mapping figure that obtains of training, have three layers full articulamentum altogether, is distributed in pyramid shape, every layer complete connect Number progressively successively decreases, and every layer of full connection neuron number is fixed as 2048.
Differ greatly from view of between global characteristics image and local feature image, this full connection mixing together side Method, compared with commonly full articulamentum directly merges, effectively reduces global characteristics image and local feature image co-registration is brought Error, realizes the abundant fusion of multiple features, lifts plankton image classification accuracy rate.
S5: whole primitive character images, global characteristics image and the local that step s1, step s2 and step s3 are obtained Characteristic image is input in this multiple features fusion convolutional neural networks model and is trained, and finally gives the multiple features after optimization and melts Conjunction convolutional neural networks model:
S51: initial state information is set first, including iterationses, learning rate and initialization mode；
S52: this multiple features fusion convolutional neural networks model is carried out with fl transmission and feeds back with backward, make this multiple features melt Close convolutional neural networks model to be trained according to the plankton image of input and learn；
S53: output loss function value and accuracy rate；
S54: lift the performance of this multiple features fusion convolutional neural networks model by reducing loss function value；
S54: judge whether to reach the iterationses of setting, if it is, training finishes, the multiple features after being optimized melt Close convolutional neural networks model, otherwise, continue to redirect execution step s52；
S6: plankton image to be sorted is input in the multiple features fusion convolutional neural networks model after optimization, According to the probability score of final output, judge the classification corresponding to plankton image.
This invention, on the premise of based on a large amount of training datas, is up in the classification accuracy of multi-class plankton image 95%.
In above-described embodiment of the application, by providing a kind of plankton based on multiple features fusion convolutional neural networks Image classification method, gathers a large amount of clearly plankton images first, builds plankton picture number multi-class on a large scale According to collection, subsequently use image conversion and Boundary extracting algorithm, extract global characteristics and local feature, by primitive character image, entirely Office's characteristic image is put into together with local feature image in the multiple features fusion convolutional neural networks of deep learning and is trained, and obtains many The convolutional neural networks model of Feature Fusion, finally, plankton image is input to this multiple features fusion convolutional neural networks In model, according to the probability score of final output, you can realize classification.The present invention is by biological morphology angle, computer vision Method is combined with deep learning technology, has very high classification particularly with multi-class plankton image on a large scale accurate Rate.
It should be pointed out that described above is not limitation of the present invention, the present invention is also not limited to the example above, Change, modification, interpolation or replacement that those skilled in the art are made in the essential scope of the present invention, also should Belong to protection scope of the present invention.

Claims (3)

1. a kind of plankton image classification method based on multiple features fusion convolutional neural networks it is characterised in that include as Lower step:
S1: collection clearly plankton image, builds plankton image data set multi-class on a large scale, wherein, this number According to the plankton image concentrated as primitive character image；
S2: process primitive character image, extract planktonic global characteristics, obtain global characteristics image, concrete process step For:
S21: using image segmentation scharr operator, primitive character image is changed, the image after conversion includes global characteristics And local feature；
S22: remove the local feature in transition diagram picture using bilateral filtering method；
S23: enhancing contrast ratio is projecting the global characteristics in transition diagram picture；
S3: by the canny edge detection algorithm of computer vision, primitive character image is processed, extract planktonic Edge texture feature, i.e. planktonic local feature, obtain local feature image；
S4: build the multiple features fusion convolutional neural networks model based on primitive character, global characteristics and local feature, how special this is Levy fusion convolutional neural networks and include three separate basic sub-networks, each basic sub-network is respectively trained primitive character Image, global characteristics image and local feature image, wherein, 1 to 5 layers of this multiple features fusion convolutional neural networks is convolution Layer, 6 to 8 layers is full articulamentum；
S5: whole primitive character images, global characteristics image and the local feature that step s1, step s2 and step s3 are obtained Image is input in this multiple features fusion convolutional neural networks and is trained, and finally gives the god of the multiple features fusion convolution after optimization Through network model:
S51: initial state information is set first, including iterationses, learning rate and initialization mode；
S52: this multiple features fusion convolutional neural networks model is carried out with fl transmission and feeds back with backward, make this multiple features fusion roll up Long-pending neural network model is trained according to the plankton image of input and learns；
S53: output loss function value and accuracy rate；
S54: lift the performance of this multiple features fusion convolutional neural networks model by reducing loss function value；
S54: judge whether to reach the iterationses of setting, the multiple features fusion volume if it is, training finishes, after being optimized Long-pending neural network model, otherwise, continues to redirect execution step s52；
S6: plankton image to be sorted is input in the multiple features fusion convolutional neural networks model after optimization, according to The probability score of final output, judges the classification corresponding to plankton image.
2. the plankton image classification method based on multiple features fusion convolutional neural networks according to claim 1, its It is characterised by, described basis sub-network uses any one the convolutional Neural net in alexnet, vggnet or googlenet Network.
3. the plankton image classification method based on multiple features fusion convolutional neural networks according to claim 1, its It is characterised by, in described full articulamentum, the feature that three basic sub-network training obtain is merged using the full cross-mixing method that connects Mapping graph.
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