Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction

Qian, Jianjun; Yang, Jian; Gao, Guangwei

doi:10.1016/j.patcog.2013.03.005

Cited by 44 publications

(16 citation statements)

References 42 publications

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“…The performance of the classification model and the classification accuracy rate depend largely on the numerical properties of various image features which represent the data of the classification model. In recent years, many feature extraction techniques have been developed and each technique has a strengths and weaknesses [7,8,9,10]. A good feature extraction technique provides relevant features.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Feature Extraction Methods in Images Classification

Medjahed¹

2015

IJIGSP

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Feature extraction is an important step in image classification. It allows to represent the content of images as perfectly as possible. However, in this paper, we present a comparison protocol of several feature extraction techniques under different classifiers. We evaluate the performance of feature extraction techniques in the context of image classification and we use both binary and multiclass classifications. The analyses of performance are conducted in term of: classification accuracy rate, recall, precision, f-measure and other evaluation measures. The aim of this research is to show the relevant feature extraction technique that improves the classification accuracy rate and provides the most implicit classification data. We analyze the models obtained by each feature extraction method under each classifier.

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Feature Extraction Methods in Images Classification

Medjahed¹

2015

IJIGSP

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“…Gabor [27], LBP [34] etc. [35][36][37] can be further applied after NOMR. In fact, our proposed representation method can be seen as a data preprocessing process, thus the state-of-the-art feature extraction methods or classifiers can be used to further enhance the performance of the classification system.…”

Section: Further Discussion On Nomrmentioning

confidence: 99%

Nearest orthogonal matrix representation for face recognition

et al. 2015

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“…Earlier MOT works mostly adopt hand-crafted features for object representation [7,8,9,10]. Color histograms are commonly used to represent object appearance in multiobject tracking [7,11], and histograms of oriented gradients (HOG) [8] is also a popular choice [12,13].…”

Section: Object Representationmentioning

confidence: 99%

Instance-aware representation learning and association for online multi-person tracking

Wang

et al. 2019

Pattern Recognition

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Multi-Person Tracking (MPT) is often addressed within the detection-to-association paradigm. In such approaches, human detections are first extracted in every frame and person trajectories are then recovered by a procedure of data association (usually offline). However, their performances usually degenerate in presence of detection errors, mutual interactions and occlusions. In this paper, we present a deep learning based MPT approach that learns instance-aware representations of tracked persons and robustly online infers states of the tracked persons. Specifically, we design a multibranch neural network (MBN), which predicts the classification confidences and locations of all targets by taking a batch of candidate regions as input. In our MBN architecture, each branch (instance-subnet) corresponds to an individual to be tracked and new branches can be dynamically created for handling newly appearing persons. Then based on the output of MBN, we construct a joint association matrix that represents meaningful states of tracked persons (e.g., being tracked or disappearing from the scene) and solve it by using the efficient Hungarian algorithm. Moreover, we allow the instance-subnets to be updated during tracking by online mining hard examples, accounting to person appearance variations over time. We comprehensively evaluate our framework on a popular MPT benchmark, demonstrating its excellent performance in comparison with recent online MPT methods.

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Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction

Cited by 44 publications

References 42 publications

A Comparative Study of Feature Extraction Methods in Images Classification

A Comparative Study of Feature Extraction Methods in Images Classification

Nearest orthogonal matrix representation for face recognition

Instance-aware representation learning and association for online multi-person tracking

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