Hybrid soft computing approaches to content based video retrieval: A brief review

Bhaumik, Hrishikesh; Bhattacharyya, Siddhartha; Nath, Mausumi Das; Chakraborty, Susanta Kumar

doi:10.1016/j.asoc.2016.03.022

Cited by 40 publications

(22 citation statements)

References 93 publications

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“…Multimedia databases, especially those for videos, created decades ago are comparatively smaller than current databases owing to the aforementioned characteristics, and annotation was performed manually based on keywords. Databases at present have ballooned in size and in the amount of video information they hold, thus establishing the need for automated video structure analysis without human involvement [ 3 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Shot boundary detection (SBD) also known as temporal video segmentation, is the first process in CBVIR, and its output significantly affects the subsequent processes. SBD performance influences the results and efficiency of the subsequent CBVIR modules, so SBD is considered a relevant stage in CBVIR [ 10 , 11 , 15 , 16 , 17 , 18 , 19 ]. The target of SBD is to partition a video into its basic units (shots) to be forwarded to the rest of the CBVIR modules for further analysis [ 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…This unavailability is due to the randomness and size of raw video data. Hence, a robust, efficient, automated SBD method is an urgent requirement [ 11 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

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Methods and Challenges in Shot Boundary Detection: A Review

Abdulhussain

Ramli

Saripan

et al. 2018

Entropy

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Abstract:The recent increase in the number of videos available in cyberspace is due to the availability of multimedia devices, highly developed communication technologies, and low-cost storage devices. These videos are simply stored in databases through text annotation. Content-based video browsing and retrieval are inefficient due to the method used to store videos in databases. Video databases are large in size and contain voluminous information, and these characteristics emphasize the need for automated video structure analyses. Shot boundary detection (SBD) is considered a substantial process of video browsing and retrieval. SBD aims to detect transition and their boundaries between consecutive shots; hence, shots with rich information are used in the content-based video indexing and retrieval. This paper presents a review of an extensive set for SBD approaches and their development. The advantages and disadvantages of each approach are comprehensively explored. The developed algorithms are discussed, and challenges and recommendations are presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Methods and Challenges in Shot Boundary Detection: A Review

Abdulhussain

Ramli

Saripan

et al. 2018

Entropy

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“…Artificial intelligence, Artificial Neural Network, Fuzzy logic and expert systems have been increasingly used in various applications in the last 30 years: Engineering design, Image recognition [2]; Prediction, Estimation, Pattern recognition, and optimization [3]; Petroleum exploration and production; civil engineering, environmental and water resources engineering, traffic engineering, highway engineering, geotechnical engineering [4]; Image classification [5]; Fingerprint analysis [6]; Software defect prediction [7]; Breast cancer identification [8]; Human action recognition, video surveillance to health-care [9]; Video retrieval [10]; Localization scheme of wireless sensor networks, military surveillance, environmental monitoring, robotics, domestics, animal tracking [11]; Image recognition of plant diseases [12]; Wind power forecasting [13]; Design and analysis of antennas [14]; Image recognition [15]; Multimodal medical image fusion [16]; Satellite data and GPS [17]; water resources engineering [18]; air traffic control [19]; financial forecasting [20]; earthquake prediction [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

Narayanakumar¹,

Raja²

2016

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The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequencymagnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the How to cite this paper: Narayanakumar, S. and Raja, K.

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“…have made great contributions to the actual applications of contemporary soft computing techniques in theoretical fields, including the predictions of daily flows, river stage, and plant leaf classification, etc. (Bhaumik, Bhattacharyya, Nath, & Chakraborty, 2016;Wang, Chau, & Xu, 2015;Wu, & Chau, 2011;Wu, Chau, & Li, 2009;) Therefore a series of optimal prediction results characterized by higher precision and better stability were obtained, which provided an important theoretical basis and reliable tool for the establishment of fuzzy prediction system and soft computing techniques undoubtedly; Moreover, based on the enlightenment of contemporary soft computing, the mechanism investigation of turbulence characteristic predictions can be greatly improved, too (Taormina & Chau, 2015). As the fuzzy prediction of AWJ characteristic streamlines the machining efficiency and controllable monitoring in industrial practices, it should be discussed thoroughly.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy prediction of AWJ turbulence characteristics by using typical multi-phase flow models

Liang

Shan

Liu

et al. 2017

Engineering Applications of Computational Fluid Mechanics

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For the purpose of improving the working performance of AWJ (Abrasive waterjet) grinding in actual machining, and revealing the unknown influence mechanism of waterjet machining prediction, this research conducted a theoretical and experimental investigation concerning with the influences caused by multi-phase flow models, on the fuzzy prediction of turbulence characteristics. First, typical flow models describing the abrasive waterjet were presented and enunciated; Second, a series of turbulence characteristics were defined to quantitatively demonstrate the waterjet flow, which are responsible for the actual performances of AWJ grinding in the contact zone; Then a new fuzzy prediction system equipped with logic protocols was established and introduced to predict turbulence characteristics based upon the machining parameters by employing different flow models, therefore a set of quantified prediction results generated from the CFD (Computational Fluid Dynamics) simulation, orthogonal experiments and actual measurements can be obtained and distinguished systematically. Through a detailed performance analysis with benchmark indexes and prediction comparisons, the theoretical superiorities and specific applications of these flow models were fully discussed and then an integrated conclusion assessing their inherent influence mechanism was acquired. Based on these innovative achievements, the instantaneous machining inspection or parameter optimization in AWJ grinding process can be facilitated, simultaneously the new research ideas aimed at waterjet monitoring or grinding improvements can be presented as well. ARTICLE HISTORY

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Hybrid soft computing approaches to content based video retrieval: A brief review

Cited by 40 publications

References 93 publications

Methods and Challenges in Shot Boundary Detection: A Review

Methods and Challenges in Shot Boundary Detection: A Review

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

Fuzzy prediction of AWJ turbulence characteristics by using typical multi-phase flow models

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