Comparative study between deep learning and bag of visual words for wild-animal recognition

Okafor, Emmanuel; Pawara, Pornntiwa; Karaaba, Faik; Surinta, Olarik; Codreanu, Valeriu; Schomaker, Lambert; Wiering, Marco A.

doi:10.1109/ssci.2016.7850111

Cited by 32 publications

(13 citation statements)

References 31 publications

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“…where y i denotes the target values y i [ {0,1}. The fraction within the log accounts for the softmax activation function (Okafor et al, 2016), which computes the probability distribution of the classes in a multi-class classification problem. Note that in this study, we investigate both binary and multi-classification problems.…”

Section: Three Inception Module Cnn Architecturementioning

confidence: 99%

Hsc70-4 Deforms Membranes to Promote Synaptic Protein Turnover by Endosomal Microautophagy

Uytterhoeven

Lauwers

Maes

et al. 2015

Neuron

143

136

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Synapses are often far from their cell bodies and must largely independently cope with dysfunctional proteins resulting from synaptic activity and stress. To identify membrane-associated machines that can engulf synaptic targets destined for degradation, we performed a large-scale in vitro liposome-based screen followed by functional studies. We identified a presynaptically enriched chaperone Hsc70-4 that bends membranes based on its ability to oligomerize. This activity promotes endosomal microautophagy and the turnover of specific synaptic proteins. Loss of microautophagy slows down neurotransmission while gain of microautophagy increases neurotransmission. Interestingly, Sgt, a cochaperone of Hsc70-4, is able to switch the activity of Hsc70-4 from synaptic endosomal microautophagy toward chaperone activity. Hence, Hsc70-4 controls rejuvenation of the synaptic protein pool in a dual way: either by refolding proteins together with Sgt, or by targeting them for degradation by facilitating endosomal microautophagy based on its membrane deforming activity.

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Section: Three Inception Module Cnn Architecturementioning

confidence: 99%

Hsc70-4 Deforms Membranes to Promote Synaptic Protein Turnover by Endosomal Microautophagy

Uytterhoeven

Lauwers

Maes

et al. 2015

Neuron

143

136

View full text Add to dashboard Cite

show abstract

“…Inspired from the original text representation model, BoF was introduced for image categorization that was represented as an unordered collection of visual words [15]. As they are represented as histograms of local descriptors, BoF gives an extremely compact description of images.…”

Section: Bag Of Features Bofmentioning

confidence: 99%

Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition

Hamid

Razali

İbrahim³

2019

IJEECS

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This paper presents a comparative study between Bag of Features (BoF), Conventional Convolutional Neural Network (CNN) and Alexnet for fruit recognition. Automatic fruit recognition can minimize human intervention in their fruit harvesting operations, operation time and harvesting cost. On the other hand, this task is very challenging because of the similarities in shapes, colours and textures among various types of fruits. Thus, a robust technique that can produce good result is necessary. Due to the outstanding performance of deep learning like CNN and its pre-trained models like AlexNet in image recognition, this paper investigates the accuracy of conventional CNN, and Alexnet in recognizing thirty different types of fruits from a publicly available dataset. Besides that, the recognition performance of BoF is also examined since it is one of the machine learning techniques that achieves good result in object recognition. The experimental results indicate that all of these three techniques produce excellent recognition accuracy. Furthermore, conventional CNN achieves the fastest recognition result compared to BoF, and Alexnet.

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“…The experiment result shows that Local Binary Patterns Histograms (LBPH) are better than Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for small training data sets. Okafor et al [4] proposed a wildlife identification method based on deep learning and visual vocabulary, which used grayscale color information and different spatial aggregation methods to complete the training process, the accuracy of which reached 99.93%. By incorporating knowledge about object similarities from visual and semantic domains during the transfer process, Tang et al [5] proposed the object similarity-based knowledge transfer method, which achieved state-of-the-art detection performance using the semi-supervised learning method.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Wildlife Recognition Model Based on Multi-Branch Aggregation and Squeeze-And-Excitation Network

Xie

Zhang

et al. 2019

Applied Sciences

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Infrared camera trapping, which helps capture large volumes of wildlife images, is a widely-used, non-intrusive monitoring method in wildlife surveillance. This method can greatly reduce the workload of zoologists through automatic image identification. To achieve higher accuracy in wildlife recognition, the integrated model based on multi-branch aggregation and Squeeze-and-Excitation network is introduced. This model adopts multi-branch aggregation transformation to extract features, and uses Squeeze-and-Excitation block to adaptively recalibrate channel-wise feature responses based on explicit self-mapped interdependencies between channels. The efficacy of the integrated model is tested on two datasets: the Snapshot Serengeti dataset and our own dataset. From experimental results on the Snapshot Serengeti dataset, the integrated model applies to the recognition of 26 wildlife species, with the highest accuracies in Top-1 (when the correct class is the most probable class) and Top-5 (when the correct class is within the five most probable classes) at 95.3% and 98.8%, respectively. Compared with the ROI-CNN algorithm and ResNet (Deep Residual Network), on our own dataset, the integrated model, shows a maximum improvement of 4.4% in recognition accuracy.

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Comparative study between deep learning and bag of visual words for wild-animal recognition

Cited by 32 publications

References 31 publications

Hsc70-4 Deforms Membranes to Promote Synaptic Protein Turnover by Endosomal Microautophagy

Hsc70-4 Deforms Membranes to Promote Synaptic Protein Turnover by Endosomal Microautophagy

Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition

An Integrated Wildlife Recognition Model Based on Multi-Branch Aggregation and Squeeze-And-Excitation Network

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