Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector

Du, Chao; Wang, Yanna; Wang, Chunheng; Shi, Cunzhao; Xiao, Baihua

doi:10.1016/j.patrec.2019.11.015

Cited by 46 publications

(21 citation statements)

References 43 publications

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“…Compared with the performance between the trainings without pre-trained weights and with pre-trained COCO weights, transfer learning could save training time without compromising detection performance. The latter training only involved the heads of FPN, RPN and detection branches, containing high-level semantics [ 36 ]. Such semantics may be more important for instance segmentation and object detection than low-level generic features extracted by the bottom architecture of the detectors [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

Developing and Evaluating Poultry Preening Behavior Detectors via Mask Region-Based Convolutional Neural Network

Xue

Lin

et al. 2020

Animals

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There is a lack of precision tools for automated poultry preening monitoring. The objective of this study was to develop poultry preening behavior detectors using mask R-CNN. Thirty 38-week brown hens were kept in an experimental pen. A surveillance system was installed above the pen to record images for developing the behavior detectors. The results show that the mask R-CNN had 87.2 ± 1.0% MIOU, 85.1 ± 2.8% precision, 88.1 ± 3.1% recall, 95.8 ± 1.0% specificity, 94.2 ± 0.6% accuracy, 86.5 ± 1.3% F1 score, 84.3 ± 2.8% average precision and 380.1 ± 13.6 ms·image−1 processing speed. The six ResNets (ResNet18-ResNet1000) had disadvantages and advantages in different aspects of detection performance. Training parts of the complex network and transferring some pre-trained weights from the detectors pre-trained in other datasets can save training time but did not compromise detection performance and various datasets can result in different transfer learning efficiencies. Resizing and padding input images to different sizes did not affect detection performance of the detectors. The detectors performed similarly within 100–500 region proposals. Temporal and spatial preening behaviors of individual hens were characterized using the trained detector. In sum, the mask R-CNN preening behavior detector could be a useful tool to automatically identify preening behaviors of individual hens in group settings.

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Section: Discussionmentioning

confidence: 99%

Developing and Evaluating Poultry Preening Behavior Detectors via Mask Region-Based Convolutional Neural Network

Xue

Lin

et al. 2020

Animals

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“…If connecting the high-level convolutional features and low-level convolutional features, we will obtain richer and more discriminative convolutional features for vein recognition. However, the study [45] indicated that directly connecting the high-level convolutional features and low-level convolutional features as images representation cannot obtain the excellent result due to the fact that low-level convolutional contain some background information.…”

Section: A the Analysis Of Convolutional Features With Vein Informationmentioning

confidence: 99%

Multi-Layer Convolutional Features Concatenation With Semantic Feature Selector for Vein Recognition

et al. 2019

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Pre-trained DCNN trained on a large-scale image database can be used as a universal feature representation for image classification, which has achieved significant progress in some image recognition tasks. Compared with other image recognition tasks, directly utilizing a single convolutional feature as feature representation for vein recognition task cannot achieve the impressive result due to the sparse distribution of vein information. Therefore, to obtain more representative and discriminative convolutional feature for vein recognition, a novel multi-layer convolutional features' concatenation with semantic feature selector is proposed in this paper. In the pre-trained DCNN, different convolutional layers can encode different-level feature information. High-level convolutional features with vein information cover more semantic information and low-level convolutional features with vein information cover more detail information. However, low-level convolutional features also contain some background information. Therefore, in order to remove the background information of low-level convolutional features, a novel semantic feature selector is presented. First, the proposed local max-pooling of preserving spatial position (LMP-PSP) information is applied on activation map obtained by adding up all feature maps of the high-level convolutional layer to generate the semantic weighting map, which reflects key vein information of high-level convolutional features. Then, semantic weighting map is regarded as a feature selector to discard the background information of the low-level convolutional features and preserve the detail information of low level convolutional features. Finally, low-level convolutional features with vein information are selectively linked to high-level convolutional features with vein information based on the proposed semantic feature selector. A series of rigorous experiments on two lab-made vein databases named CUMT-Hand-Dorsa Vein database and CUMT-Palm Vein database is conducted to verify the effectiveness and feasibility of the proposed model. Besides, additional experiments with PUT Palm Vein database and the subset of PolyU database illustrate its generalization ability and robustness.

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“…It is equally important to mention that many promising methods based on multimodel/multimodal deep learning have been applied to image classification [21][22][23][24][25][26][27]. ere are two commonly multimodel strategies: feature fusion and ensembles of multiple classifiers.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

“…However, it can lead to "the curse of dimensionality" problem and does not guarantee the optimal accuracy due to the difference of feature ranges. Otherwise, combining high-level information with low-level information features can introduce the background clutter and semantic ambiguity due to the appearance of artifacts [24]. In the ensembles of multiple classifiers, each single classifier independently performs its task.…”

Section: Computational Intelligence and Neurosciencementioning

confidence: 99%

Leaf Recognition Based on Joint Learning Multiloss of Multimodel Convolutional Neural Networks: A Testing for Vietnamese Herb

Dat

Truong

et al. 2021

Computational Intelligence and Neuroscience

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A new modification of multi-CNN ensemble training is investigated by combining multiloss functions from state-of-the-art deep CNN architectures for leaf image recognition. We first apply the U-Net model to segment leaf images from the background to improve the performance of the recognition system. Then, we introduce a multimodel approach based on a combination of loss functions from the EfficientNet and MobileNet (called as multimodel CNN (MMCNN)) to generalize a multiloss function. The joint learning multiloss model designed for leaf recognition allows each network to perform its task and cooperate with the others simultaneously, where knowledge from various trained deep networks is shared. This cooperation-proposed multimodel is forced to deal with more complicated problems rather than a simple classification. Therefore, the network can learn much rich information and improve its generalization capability. Furthermore, a multiloss trade-off strategy between two deep learning models can reduce the effect of redundancy problems in ensemble classifiers. The performance of our approach is evaluated by our custom Vietnamese herbal leaf species dataset, and public datasets such as Flavia, Leafsnap, and Folio are used to build test cases. The results confirm that our approach enhances the leaf recognition performance and outperforms the current standard single networks while having less low computation cost.

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Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector

Cited by 46 publications

References 43 publications

Developing and Evaluating Poultry Preening Behavior Detectors via Mask Region-Based Convolutional Neural Network

Developing and Evaluating Poultry Preening Behavior Detectors via Mask Region-Based Convolutional Neural Network

Multi-Layer Convolutional Features Concatenation With Semantic Feature Selector for Vein Recognition

Leaf Recognition Based on Joint Learning Multiloss of Multimodel Convolutional Neural Networks: A Testing for Vietnamese Herb

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