Content-Based Video Retrieval Using Dominant Color and Shape Feature

Potluri, Tejaswi; Sravani, T.; Ramakrishna, Bharath; Nitta, Gnaneswara Rao

doi:10.1007/978-981-10-2471-9_36

Cited by 8 publications

(3 citation statements)

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“…Kennedy et al [10] have discussed the importance of Map-Reduce approach by labeling millions of images and applying the Nearest Neighbour algorithm to it. Shi et al, [11] also explained the same usage of Map-Reduce for CBIR [27,28] for applying nearly 400,000 images almost. Mohammed [12] explained the concept of high-performance computing and also analyzing the performance of remote sensing data using Hadoop.…”

Section: Related Workmentioning

confidence: 98%

Distributed Framework for Processing High Resolution Remote Sensing Images

Nagaraju*,

Suneetha

2019

IJEAT

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Now-a-days, sensing of remote satellite data processing is a very challenging task. The current development of satellite technology has led to explosive growth in quantity as well as the quality of the High-Resolution Remote Sensing (HRRS) images. These images can sometimes be in Gigabytes and Terabytes, which is heavy to load into the memory and also takes more time for processing. To address the challenges of processing HRRS images, a distributed map Reduce framework is proposed in this paper. This paper reflects Map-reduce as a distributed model using the Hadoop framework for processing large amounts of images. To process large amounts of images, block-based and size-based methods are introduced for effective processing. From the experiments, the proposed framework has proven to be effective in performance and speed.

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Section: Related Workmentioning

confidence: 98%

Distributed Framework for Processing High Resolution Remote Sensing Images

Nagaraju*,

Suneetha

2019

IJEAT

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“…An efficient content based video retrieval system was designed in [19] with aid of color feature for achieving effective result in video retrieval. However, retrieving similar videos was remained unsolved.…”

Section: Related Workmentioning

confidence: 99%

GA based Ensemble Classifier for Efficient Visual Content Information Retrieval

Vijayan¹,

Chandrasekar²

2017

IJCA

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Visual Content based Information retrieval has been one of the demanding research areas in the field of image and video retrieval. Few research works have been developed for video retrieval with aid of classification techniques. But, performance of conventional visual content based video retrieval methods was not efficient. A Gene Based Similarity Threshold Classifier (GSTC) Technique is proposed in order to improve the performance of visual content based video retrieval with higher precision, recall, F1 Measure and minimum time complexity. The GSTC Technique used Jaccard similarity coefficient to find out relevant videos in a given dataset based on query video clip. After identifying the relevant videos, GSTC Technique applied similarity threshold classifier in order to classify the videos into a different class based on diverse similarity threshold value with improved classification accuracy. Finally, GSTC Technique used genetic algorithm in order to discover the optimal similarity threshold value in population with aid of measured fitness function. This in turns, more similar related to query video are obtained for efficient video retrieval. The GSTC Technique conducts the experimental works on metrics such as classification accuracy, time complexity, F-measure, Precision and recall using three datasets.

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“…The authors in [2] state that the percentage of students indulging in mischievous behavior has been increased drastically during these online modes. Semi-automated proctoring software restricting the examinee to not to open any other browsers during the exam and also take the snaps of the students in random intervals to capture the behavior through various video Processing techniques like dominant Color [36], SURF, HARRIS, BRISK features [35], Color and shape features [37]. Automated Online proctoring systems uses Artificial Intelligence and Deep Learning techniques to identify the attentiveness of learner/examinee in virtual learning.…”

Section: Introductionmentioning

confidence: 99%

An automated online proctoring system using attentive-net to assess student mischievous behavior

Potluri

Venkatramaphanikumar

2023

Multimed Tools Appl

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In recent years, the pandemic situation has forced the education system to shift from traditional teaching to online teaching or blended learning. The ability to monitor remote online examinations efficiently is a limiting factor to the scalability of this stage of online evaluation in the education system. Human Proctoring is the most used common approach by either asking learners to take a test in the examination centers or by monitoring visually asking learners to switch on their camera. However, these methods require huge labor, effort, infrastructure, and hardware. This paper presents an automated AI-based proctoring system- ‘Attentive system’ for online evaluation by capturing the live video of the examinee. Our Attentive system includes four components to estimate the malpractices such as face detection, multiple person detection, face spoofing, and head pose estimation. Attentive Net detects the faces and draws bounding boxes along with confidences. Attentive Net also checks the alignment of the face using the rotation matrix of Affine Transformation. The face net algorithm is combined with Attentive-Net to extract landmarks and facial features. The process for identifying spoofed faces is initiated only for aligned faces by using a shallow CNN Liveness net. The head pose of the examiner is estimated by using the SolvePnp equation, to check if he/she is seeking help from others. Crime Investigation and Prevention Lab (CIPL) datasets and customized datasets with various types of malpractices are used to evaluate our proposed system. Extensive Experimental results demonstrate that our method is more accurate, reliable and robust for proctoring system that can be practically implemented in real time environment as Automated proctoring System. An improved accuracy of 0.87 is reported by authors with the combination of Attentive Net, Liveness net and head pose estimation.

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Content-Based Video Retrieval Using Dominant Color and Shape Feature

Cited by 8 publications

References 1 publication

Distributed Framework for Processing High Resolution Remote Sensing Images

Distributed Framework for Processing High Resolution Remote Sensing Images

GA based Ensemble Classifier for Efficient Visual Content Information Retrieval

An automated online proctoring system using attentive-net to assess student mischievous behavior

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