Cowbree: A novel dataset for fine-grained visual categorization

Khan, Umar Akbar; Din, Saira Moin U.; Lashari, Saima Anwar; Saare, Murtaja Ali; Ilyas, Muhammad

doi:10.11591/eei.v9i5.2443

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…These vectors allow different features to be assembled into a compact vector [13]. Principal component analysis (PCA) also allows the dimension of the feature vector to be reduced [2,7,21]. The authors in [22] studied the different reductions related to PCA applied to scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF) descriptors on Wang (http://wang.ist.psu.edu/docs/ related/, accessed on 20 March 2021) and Coil100 (https://www1.cs.columbia.edu/CAVE/ software/softlib/coil-100.php, accessed on 20 March 2021) databases and concluded that the optimal reduction was at 70%.…”

Section: Dimensionality Reductionmentioning

confidence: 99%

A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network

2022

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With the emergence of smartphones, video surveillance cameras, social networks, and multimedia engines, as well as the development of the internet and connected objects (the Internet of Things—IoT), the number of available images is increasing very quickly. This leads to the necessity of managing a huge amount of data using Big Data technologies. In this context, several sectors, such as security and medicine, need to extract image features (index) in order to quickly and efficiently find these data with high precision. To reach this first goal, two main approaches exist in the literature. The first one uses classical methods based on the extraction of visual features, such as color, texture, and shape for indexation. The accuracy of these methods was acceptable until the early 2010s. The second approach is based on convolutional neuronal networks (CNN), which offer better precision due to the largeness of the descriptors, but they can cause an increase in research time and storage space. To decrease the research time, one needs to reduce the size of these vectors (descriptors) by using dimensionality reduction methods. In this paper, we propose an approach that allows the problem of the “curse of dimensionality” to be solved thanks to an efficient combination of convolutional neural networks and dimensionality reduction methods. Our contribution consists of defining the best combination approach between the CNN layers and the regional maximum activation of convolutions (RMAC) method and its variants. With our combined approach, we propose providing reduced descriptors that will accelerate the research time and reduce the storage space while maintaining precision. We conclude by proposing the best position of an RMAC layer with an increase in accuracy ranging from 4.03% to 27.34%, a decrease in research time ranging from 89.66% to 98.14% in the function of CNN architecture, and a reduction in the size of the descriptor vector by 97.96% on the GHIM-10K benchmark database.

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Section: Dimensionality Reductionmentioning

confidence: 99%

A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network

2022

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“…KNN is used for implementing the feature similarity to predict the value for new data point. It can also match the training dataset for analysing state assumptions [40]. Working procedure for KNN:…”

Section: 4 Knn Classifiermentioning

confidence: 99%

Performance analysis of demand forecasting in energy consumption based on ensemble model

Dhanalakshmi

Ayyanathan

2022

Bulletin EEI

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Over the previous decade, energy usage has increased exponentially all over the world. The machine learning algorithms are used to classify the demand and requirement of off and evening peak load of southern regional load dispatch centre (SRLDC) data. In this paper, data are classified based on demand and requirement of both evening and off peak of day wise southern regional grid of Andhra Pradesh, Karnataka, Kerala, Tamilnadu, and Pondicherry of different states are proposed. The machine learning algorithms like k-nearest neighbors (KNN), random forest, and logistic regression have been adopted to classify the model. To improve this model efficiency, an ensemble learning method is used to increase the accuracy. The performance measure of state-wise outcome is determined by classifying its demand and requirement needs over its state energy consumption and with different classification algorithms and it is improved by using a combined method of ensemble model with accuracy of 86%.

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An Intelligence Cattle Reidentification System Over Transport by Siamese Neural Networks and YOLO

Bakhshayeshi,

Erfani,

Taghikhah

et al. 2024

IEEE Internet Things J.

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Cowbree: A novel dataset for fine-grained visual categorization

Cited by 3 publications

References 17 publications

A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network

A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network

Performance analysis of demand forecasting in energy consumption based on ensemble model

An Intelligence Cattle Reidentification System Over Transport by Siamese Neural Networks and YOLO

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