High-Level Feature Extraction for Classification and Person Re-Identification

Feizi, A.

doi:10.1109/jsen.2017.2756349

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…Feature extraction is one of the essential processes of computer vision and imageprocessing tasks [42]. Feature extraction can be understood in many ways: low-level extraction, which focuses on edges, colours, textures, shapes, regions and other characteristics of an image, sometimes extracted through transforms such as Fourier or Discrete Cosine Transform [43][44][45] ; high-level extraction that jumps to understanding or behaviours [46] and also to the reduction of dimensionality, which sometimes is accomplished by selecting a reduced set of features or measurements from the data [47].…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Gemstone Classification Using Computer Vision

Chow

Reyes-Aldasoro

2021

Minerals

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This paper presents a computer-vision-based methodology for automatic image-based classification of 2042 training images and 284 unseen (test) images divided into 68 categories of gemstones. A series of feature extraction techniques (33 including colour histograms in the RGB, HSV and CIELAB space, local binary pattern, Haralick texture and grey-level co-occurrence matrix properties) were used in combination with different machine-learning algorithms (Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbour, Decision Tree, Random Forest, Naive Bayes and Support Vector Machine). Deep-learning classification with ResNet-18 and ResNet-50 was also investigated. The optimal combination was provided by a Random Forest algorithm with the RGB eight-bin colour histogram and local binary pattern features, with an accuracy of 69.4% on unseen images; the algorithms required 0.0165 s to process the 284 test images. These results were compared against three expert gemmologists with at least 5 years of experience in gemstone identification, who obtained accuracies between 42.6% and 66.9% and took 42–175 min to classify the test images. As expected, the human experts took much longer than the computer vision algorithms, which in addition provided, albeit marginal, higher accuracy. Although these experiments included a relatively low number of images, the superiority of computer vision over humans is in line with what has been reported in other areas of study, and it is encouraging to further explore the application in gemmology and related areas.

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Section: Feature Extractionmentioning

confidence: 99%

Automatic Gemstone Classification Using Computer Vision

Chow

Reyes-Aldasoro

2021

Minerals

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“…Asghar et al [22] introduced a novel algorithm for highlevel feature extraction and used those features for classification and re-identification. Their proposed method is a two-tier approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

Butt¹,

Letchmunan²,

Hafinaz³

et al. 2020

IJACSA

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The meteoric growth of data over the internet from the last few years has created a challenge of mining and extracting useful patterns from a large dataset. In recent years, the growth of digital libraries and video databases makes it more challenging and important to extract useful information from raw data to prevent and detect the crimes from the database automatically. Street crime snatching and theft detection is the major challenge in video mining. The main target is to select features/objects which usually occurs at the time of snatching. The number of moving targets imitates the performance, speed and amount of motion in the anomalous video. The dataset used in this paper is Snatch 101; the videos in the dataset are further divided into frames. The frames are labelled and segmented for training. We applied the VGG19 Convolutional Neural Network architecture algorithm and extracted the features of objects and compared them with original video features and objects. The main contribution of our research is to create frames from the videos and then label the objects. The objects are selected from frames where we can detect anomalous activities. The proposed system is never used before for crime prediction, and it is computationally efficient and effective as compared to state-of-the-art systems. The proposed system outperformed with 81 % accuracy as compared to stateof-the-art systems.

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“…On the other hand, it can effectively reflect the characteristics of input data. It is possible to greatly improve the accuracy and robustness of the prediction model in many tasks by extracting high dimensional space vectors instead of the original input vectors (Feizi, 2017).…”

Section: Deep Belief Networkmentioning

confidence: 99%

A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Chen

Zhang

2019

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Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

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High-Level Feature Extraction for Classification and Person Re-Identification

Cited by 11 publications

References 34 publications

Automatic Gemstone Classification Using Computer Vision

Automatic Gemstone Classification Using Computer Vision

Detecting Video Surveillance Using VGG19 Convolutional Neural Networks

A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

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