Recognition of Car Makes and Models From a Single Traffic-Camera Image

He, Hongsheng; Shao, Zhenzhou; Tan, Jindong

doi:10.1109/tits.2015.2437998

Cited by 72 publications

(40 citation statements)

References 21 publications

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“…Object recognition has been one of the fundamental areas in pattern recognition for decades, producing prosperous results in specific object recognition, such as faces [4] and cars [5], [6]. Food recognition is challenging as compared to specific object recognition because it is essentially an intraclass recognition problem.…”

Section: Introductionmentioning

confidence: 99%

DietCam: Multiview Food Recognition Using a Multikernel SVM

Kong

Tan

2016

IEEE J. Biomed. Health Inform.

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Food recognition is a key component in evaluation of everyday food intakes, and its challenge is due to intraclass variation. In this paper, we present an automatic food classification method, DietCam, which specifically addresses the variation of food appearances. DietCam consists of two major components, ingredient detection and food classification. Food ingredients are detected through a combination of a deformable part-based model and a texture verification model. From the detected ingredients, food categories are classified using a multiview multikernel SVM. In the experiment, DietCam presents reliability and outperformance in recognition of food with complex ingredients on a database including 15,262 food images of 55 food types.

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Section: Introductionmentioning

confidence: 99%

DietCam: Multiview Food Recognition Using a Multikernel SVM

Kong

Tan

2016

IEEE J. Biomed. Health Inform.

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“…For the color feature, we extract RGB histograms as descriptors; for the texture feature, we extract the LBP (Local Binary Pattern) [Satpathy et al 2014;He et al 2015] and PHOG (Pyramid Histogram of Oriented Gradients) [Dalal and Triggs 2005] histograms as descriptors. The radius, number of sample points, and histogram bins of pattern arrangement of LBP are 1, 8, and 256, respectively.…”

Section: Feature Extractionmentioning

confidence: 99%

Multi-Class Latent Concept Pooling for Computer-Aided Endoscopy Diagnosis

Wang

Cong

Fan

et al. 2017

ACM Trans. Multimedia Comput. Commun. Appl.

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Successful computer-aided diagnosis systems typically rely on training datasets containing sufficient and richly annotated images. However, detailed image annotation is often time consuming and subjective, especially for medical images, which becomes the bottleneck for the collection of large datasets and then building computer-aided diagnosis systems. In this article, we design a novel computer-aided endoscopy diagnosis system to deal with the multi-classification problem of electronic endoscopy medical records (EEMRs) containing sets of frames, while labels of EEMRs can be mined from the corresponding text records using an automatic text-matching strategy without human special labeling. With unambiguous EEMR labels and ambiguous frame labels, we propose a simple but effective pooling scheme called Multi-class Latent Concept Pooling, which learns a codebook from EEMRs with different classes step by step and encodes EEMRs based on a soft weighting strategy. In our method, a computer-aided diagnosis system can be extended to new unseen classes with ease and applied to the standard single-instance classification problem even though detailed annotated images are unavailable. In order to validate our system, we collect 1,889 EEMRs with more than 59K frames and successfully mine labels for 348 of them. The experimental results show that our proposed system significantly outperforms the state-of-the-art methods. Moreover, we apply the learned latent concept codebook to detect the abnormalities in endoscopy images and compare it with a supervised learning classifier, and the evaluation shows that our codebook learning method can effectively extract the true prototypes related to different classes from the ambiguous data.

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“…Due to the high dimensionality and relatively slow computational speed of SIFT, some works have adopted the Speeded Up Robust Features (SURF [13]) (e.g., [5], [10]) and the Histogram of Oriented Gradients (HOG [14]) (e.g., in [5] and [15]). Other features based on edges, gradients or corners (e.g., by [2], [3], [16]), and MPEG-7 descriptors such as Edge Histograms [17], [18] (e.g., by [10]) were also explored for VMMR purposes. In most approaches, the raw features are embedded into global representations of vehicle makes and models ( [3], [5], [10], [12], [15]) as shown in Table I.…”

Section: B Features Extraction and Global Features Representationmentioning

confidence: 99%

“…On the other hand, Munroe and Madden [3] use machine learning algorithms such as C4.5 Decision Trees, k-Nearest Neighbors (kNN), and Feed-forward Neural Networks as classifiers for VMMR. He et al [16] built an ensemble of neural networks for classification and also tested kNN, AdaBoost, and SVM. However, such approaches based on edges from images suffer greatly in cases of occlusions, and hence are not applicable in real-life scenarios [3].…”

Section: Classification Approachesmentioning

confidence: 99%

Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features

Siddiqui

Mammeri

Boukerche

2016

IEEE Trans. Intell. Transport. Syst.

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In this paper, we propose and evaluate unexplored approaches for real-time automated vehicle make and model recognition (VMMR) based on a bag of speeded-up robust features (BoSURF) and demonstrate the suitability of these approaches for vehicle identification systems. The proposed approaches use SURF features of vehicles' front-or rear-facing images and retain the dominant characteristic features (codewords) in a dictionary. Two schemes of dictionary building are evaluated: "single dictionary" and "modular dictionary." Based on the optimized dictionaries, the SURF features of vehicles' front-or rear-face images are embedded into BoSURF histograms, which are used to train multiclass support vector machines (SVMs) for classification. Two real-time VMMR classification schemes are proposed and evaluated: a single multiclass SVM and an ensemble of multiclass SVM based on attribute bagging. The processing speed and accuracy of the VMMR system are affected greatly by the size of the dictionary. The tradeoff between speed and accuracy is studied to determine optimal dictionary sizes for the VMMR problem. The effectiveness of our approaches is demonstrated through cross-validation tests on a recent publicly accessible VMMR data set. The experimental results prove the superiority of our work over the state of the art, in terms of both processing speed and accuracy, making it highly applicable to real-time VMMR systems.

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Recognition of Car Makes and Models From a Single Traffic-Camera Image

Cited by 72 publications

References 21 publications

DietCam: Multiview Food Recognition Using a Multikernel SVM

DietCam: Multiview Food Recognition Using a Multikernel SVM

Multi-Class Latent Concept Pooling for Computer-Aided Endoscopy Diagnosis

Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features

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