Video scene analysis: an overview and challenges on deep learning algorithms

Abbas, Qaisar; Ibrahim, Mostafa E. A.; Jaffar, M. Arfan

doi:10.1007/s11042-017-5438-7

Cited by 53 publications

(24 citation statements)

References 112 publications

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“…Given sufficient image quality, robust object detection has a relatively high success rate. Video scene analysis is the process of recognising and analysing humans and objects from live-streamed or recorded video scenes, in an attempt to recognise human action, classify activities, and interpret scenes [5]. Relatively unsophisticated scene analysis can easily be accomplished with modern technology through intelligent automation.…”

Section: Video Analysis As An Industry 40 Methodsmentioning

confidence: 99%

Synthesis and Evaluation of an Industry 4.0 Control Room

Hugo

Holm

2020

SAJIE

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Section: Video Analysis As An Industry 40 Methodsmentioning

confidence: 99%

Synthesis and Evaluation of an Industry 4.0 Control Room

Hugo

Holm

2020

SAJIE

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“…It can be thought of as a multilayered hierarchical architecture that attempts to learn high level abstractions in the data. There are three main reasons for the popularity of deep learning: (I) A rapid degree of improvement in the chip processing power, e.g., graphical processing units (GPUs); (II) lowered cost of computing hardware availability; and (III) considerable innovations in the machine learning methods [134,135].…”

Section: Latest Trendsmentioning

confidence: 99%

“…Finally, some other layers convert these segments into the classification/recognition of images. All these layers learn features from the input data using learning procedures and without expert intervention [135][136][137][138].…”

Section: Latest Trendsmentioning

confidence: 99%

“…Each neuron outcome is then mixed to maintain overlapping among input areas to better represent the original image information. This procedure is pursued for all layers until desirable results are achieved [135][136][137][138][139][140][141][142]. [145] CNN model Detection of exudates -- [113] Multiscale and CNN Detection of fovea and OD -AC: 97%…”

Section: Latest Trendsmentioning

confidence: 99%

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Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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“…Content-based methods [8]- [10], [20]- [23], which retrieve contents by computing similarities in content spaces, have recently attracted attention with the development of deep learning techniques [24], [25]. Since content-based methods do not rely on annotated information but directly use content information, they tend to overcome the above-mentioned problems [26], [27]. However, input query contents must be provided to perform the retrieval in the content-based methods.…”

Section: Introductionmentioning

confidence: 99%

Query is GAN: Scene Retrieval With Attentional Text-to-Image Generative Adversarial Network

et al. 2019

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Scene retrieval from input descriptions has been one of the most important applications with the increasing number of videos on the Web. However, this is still a challenging task since semantic gaps between features of texts and videos exist. In this paper, we try to solve this problem by utilizing a textto-image Generative Adversarial Network (GAN), which has become one of the most attractive research topics in recent years. The text-to-image GAN is a deep learning model that can generate images from their corresponding descriptions. We propose a new retrieval framework, ''Query is GAN'', based on the textto-image GAN that drastically improves scene retrieval performance by simple procedures. Our novel idea makes use of images generated by the text-to-image GAN as queries for the scene retrieval task. In addition, unlike many studies on text-to-image GANs that mainly focused on the generation of high-quality images, we reveal that the generated images have reasonable visual features suitable for the queries even though they are not visually pleasant. We show the effectiveness of the proposed framework through experimental evaluation in which scene retrieval is performed from real video datasets. INDEX TERMS Scene retrieval, deep learning, generative adversarial network, text-to-image translation.

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Video scene analysis: an overview and challenges on deep learning algorithms

Cited by 53 publications

References 112 publications

Synthesis and Evaluation of an Industry 4.0 Control Room

Synthesis and Evaluation of an Industry 4.0 Control Room

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Query is GAN: Scene Retrieval With Attentional Text-to-Image Generative Adversarial Network

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