Minimizing Vehicle Re-Identification Dataset Bias Using Effective Data Augmentation Method

Jamali, Zakria; Deng, Jianhua; Cai, Jingye; Aftab, Muhammad Umar; Hussain, Kashif

doi:10.1109/skg49510.2019.00030

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…[25][26][27] Additionally, the proposed deep neural network can be used for other real-time applications such as vehicle classification. [28][29][30][31]…”

Section: Experiments and Resultsmentioning

confidence: 99%

Meme Generation Using Deep Neural Network to Engage Viewers on Social Media

Alandjani

Bouk

2021

Yanbu Journal of Engineering and Science

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In today’s technological era, most new generations are using memes because the phenomenon of memes is rapidly gaining in popularity. In addition, various modifications and parodies use social and cultural phenomena to transform an original idea into another. Virus memes are sometimes called virus scripts and are the most widespread on the Internet. However, there is little scientific evidence for this assumption. This paper presents an innovative method to generate memes using machine learning algorithms that match the users’ humorous and relevant captions. The VGG16 network is used to return the embedded image. We have collected feedback from citizens of more than five countries and received 37 feedbacks to find humorous memes. The obtained results from feedback are very encouraging from the generated memes.

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“…[25][26][27] Additionally, the proposed deep neural network can be used for other real-time applications such as vehicle classification. [28][29][30][31]…”

Section: Experiments and Resultsmentioning

confidence: 99%

Meme Generation Using Deep Neural Network to Engage Viewers on Social Media

Alandjani

Bouk

2021

Yanbu Journal of Engineering and Science

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“…28,29 Additionally, the proposed deep neural network can be used for other real-time applications such as vehicle classification. [30][31][32]…”

Section: Discussionmentioning

confidence: 99%

Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

Korai

Bouk

Sindhi

2021

Yanbu Journal of Engineering and Science

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A movie poster image is one of the important media in the filmmaking process, providing valuable information about the movie, such as movie titles, characters, and genres. Identifying a movie genre from a poster can be a daunting task, as it can relate to multiple genres. To solve this problem, this paper uses a deep feedforward neural network to classify movie genres from movie poster images. In this regard, we used and trained a state-of-the-art InceptionV3 deep neural network. The network is trained on our dataset consisting of 36,423 movie poster images taken from the IMDB website, which is categorized into 28 genres. The model predicts the top three classes with the highest probability of a particular movie poster.

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“…We cannot avoid some factors like occlusion, background clutter, change in illumination etc. to evaluate the approach [99]. However, multiple benchmark datasets are available, some well-known like VeRi-776, VehicleID, etc.…”

Section: Vehicle Re-identification Benchmark Datasetsmentioning

confidence: 99%

Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review

Zakria¹,

Deng²,

Khokhar³

et al. 2021

Mathematics

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Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.

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Minimizing Vehicle Re-Identification Dataset Bias Using Effective Data Augmentation Method

Cited by 6 publications

References 7 publications

Meme Generation Using Deep Neural Network to Engage Viewers on Social Media

Meme Generation Using Deep Neural Network to Engage Viewers on Social Media

Movie Genre Classification from RGB Movie Poster Image Using Deep Feed-forward Network

Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive Review

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