A car-face region-based image retrieval method with attention of SIFT features

Zhang, Changyou; Wang, Xiaoya; Feng, Jun; Cheng, Yu; Guo, Chunyu

doi:10.1007/s11042-016-3372-8

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…Our method takes advantage of the vehicle annual inspection labels to get fine‐grained features. Compared with other three retrieval methods, the improvement of our method is that the colour information are both extracted from vehicle annual inspection labels and the whole vehicles rather than simply gotten from coarse‐grained features like three other methods [21, 33, 34].…”

Section: Resultsmentioning

confidence: 99%

Colour fast‐match for precise vehicle retrieval

Wei

Wang

Liu

et al. 2020

J. eng.

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Section: Resultsmentioning

confidence: 99%

Colour fast‐match for precise vehicle retrieval

Wei

Wang

Liu

et al. 2020

J. eng.

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“…Zapletal and Herout [24] and Chen et al [25] used HOG descriptors, Cabrera et al [20] used HAAR descriptors, while Cormier et al [26] used Local Binary Patterns (LBP) [22] -all these works also combined other hand-crafted descriptors. Zhang et al [27] used Scale-Invariant Feature Transform (SIFT) [21] to distinguish between subordinate categories with similar visual appearance, caused by a huge number of car design and models with similar appearance. In particular, SIFT was widely explored to extract distinctive key points from the vehicle for feature correspondence [28].…”

Section: Related Workmentioning

confidence: 99%

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

et al. 2021

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This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream Convolutional Neural Network (CNN) that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for Optical Character Recognition (OCR) to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the twostream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https:// github.com/ icarofua/ vehicle-rear.INDEX TERMS Vehicle identification, vehicle matching, multi-stream neural networks, feature fusion.

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“…In recent years, with the rapid development of global information technology and the accelerated popularization of artificial intelligence applications, image retrieval has become important to applications in many fields such as internet technology (Naveed et al , 2018), science education (Guzmán et al , 2017), aeronautics and astronautics (Bondur and Murynin, 2016), national defense and military (Sarkar and Acton, 2016), medicine and health (Muramatsu, 2018), traffic management (Zhang et al , 2017) and agricultural production (Kebapc and Kebapci, 2012). It has also become one of the most important research directions in the field of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Review of image low-level feature extraction methods for content-based image retrieval

Wang

Han

Jin

2019

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Purpose In the past few decades, the content-based image retrieval (CBIR), which focuses on the exploration of image feature extraction methods, has been widely investigated. The term of feature extraction is used in two cases: application-based feature expression and mathematical approaches for dimensionality reduction. Feature expression is a technique of describing the image color, texture and shape information with feature descriptors; thus, obtaining effective image features expression is the key to extracting high-level semantic information. However, most of the previous studies regarding image feature extraction and expression methods in the CBIR have not performed systematic research. This paper aims to introduce the basic image low-level feature expression techniques for color, texture and shape features that have been developed in recent years. Design/methodology/approach First, this review outlines the development process and expounds the principle of various image feature extraction methods, such as color, texture and shape feature expression. Second, some of the most commonly used image low-level expression algorithms are implemented, and the benefits and drawbacks are summarized. Third, the effectiveness of the global and local features in image retrieval, including some classical models and their illustrations provided by part of our experiment, are analyzed. Fourth, the sparse representation and similarity measurement methods are introduced, and the retrieval performance of statistical methods is evaluated and compared. Findings The core of this survey is to review the state of the image low-level expression methods and study the pros and cons of each method, their applicable occasions and certain implementation measures. This review notes that image peculiarities of single-feature descriptions may lead to unsatisfactory image retrieval capabilities, which have significant singularity and considerable limitations and challenges in the CBIR. Originality/value A comprehensive review of the latest developments in image retrieval using low-level feature expression techniques is provided in this paper. This review not only introduces the major approaches for image low-level feature expression but also supplies a pertinent reference for those engaging in research regarding image feature extraction.

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A car-face region-based image retrieval method with attention of SIFT features

Cited by 11 publications

References 30 publications

Colour fast‐match for precise vehicle retrieval

Colour fast‐match for precise vehicle retrieval

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

Review of image low-level feature extraction methods for content-based image retrieval

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