An exhaustive study on data mining techniques in mining of Multimedia database

Yadav, Pramod Kumar; Rizvi, Syed A.

doi:10.1109/icicict.2014.6781339

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…In [15], Bayes' hypothesis is exploited with basic freedom guesses among highlights. The augmentation of data set application to deal with sight and sound articles requires synchronization of various media information streams [16]. Peter et al [17] portray the significance of pre-preparing information in the web use mining and the effect of errors to the investigation of the information dur-ing this stage.…”

Section: Related Workmentioning

confidence: 99%

A New Multimedia Web-Data Mining Approach based on Equivalence Class Evaluation Pipelined to Feature Maps onto Planar Projection

Mogili,

Naidu,

Narsimha

2023

IJCAI

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Multimedia information are semi-organized or unstructured information elements whose essential substance is separately or by and large utilized for correspondence. Sight and sound information mining recognizes, arranges, and recovers important highlights from an assortment of media to recognize enlightening examples furthermore, connections for information acquisition. Computer Vision (CV)-based systems have been increasingly popular in recent years, owing to the growing number and complexity of datasets. In CV, finding meaningful photos in a huge dataset is a difficult task to solve. Traditional search engines retrieve photos based on text such as captions and metadata, but this strategy can result in a lot of irrelevant output, not to speak the time, effort, and money required to tag this textual data. In this paper, we proposed a pipelined deep learning oriented methodology framework for multimedia webdata mining based on content extracted feature maps in planner projection as input. Color, texture, form, and other high-level properties of images are represented as numerical feature vectors. This technique is based on the following computer vision tasks in general i.e., Image segmentation, Image classification, Object detection etc. In order to prove the computational efficiency and to validate its statistical behaviour, we have also presented the experimental evaluation on an standard multimedia dataset. The obtained performance results are then compared with some significant existing approaches in the terms of various statistical measures/parameters. Povzetek: Predstavljena je metoda rudarja multimedijev z globokim učenjem, ki temelji na lastnostih vsebine slik. Uporablja se za različne naloge računalniškega vida, kot so segmentacija, klasifikacija in zaznavanje objektov. Preizkušena je bila na standardnem multimedialnem naboru podatkov.

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Section: Related Workmentioning

confidence: 99%

A New Multimedia Web-Data Mining Approach based on Equivalence Class Evaluation Pipelined to Feature Maps onto Planar Projection

Mogili,

Naidu,

Narsimha

2023

IJCAI

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“…Spatial data mining concerns development and application of novel computational techniques to analyze very large spatial databases (Koperski et al, 1996;Buttenfield et al, 2001;Mennis and Guo, 2009;Yadav and Rizvi, 2014). A major distinction of spatial data mining is that attributes of the neighboring objects influence each other and thus must be taken in to account.…”

Section: Spatial Data Miningmentioning

confidence: 99%

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

Karimipour¹,

Kanani‐Sadat²

2016

J ENVIRON INFORM

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ABSTRACT. The prevalence of allergic diseases has highly increased in recent decades due to contamination of the environment with the allergy stimuli. A common treat is identifying the allergy stimulus, and then avoiding the patient to be exposed with it. There are, however, many unknown allergic diseases stimuli that are related to the characteristics of the living environment. In this article, we focus on the effect of air pollution on asthmatic allergies and investigate the association between prevalence of such allergies with those characteristics of the environment that may affect the air pollution. This investigation, eventually, leads to map the vulnerability of asthmatic allergy prevalence based on environmental characteristics. For this, spatial association rule mining has been deployed to mine the association between spatial distribution of allergy prevalence and the air pollution parameters such as CO, SO2, NO2, PM10, PM2.5, and O3 (compiled by the air pollution monitoring stations) as well as living distance to parks and roads. The categories of attributes have been defined as fuzzy sets in order to handle the data uncertainty. The results for the case study (i.e., Tehran metropolitan area) indicates that distance to parks and roads as well as CO, NO2, PM10, and PM2.5 is related to the allergy prevalence in December (the most polluted month of the year in Tehran), while SO2 and O3 have no effect on that. In June, however, the distance to parks and roads as well as NO2, PM10, and PM2.5 affect the allergy prevalence, but CO, SO2 and O3 are ineffective.

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“…beberapa tahun terakhir, pemrosesan data multimedia di komputer dan transmisi melalui jaringan berkembang pesat [1]. Dalam pencarian atau kueri informasi tekstual, ada beberapa metode yang biasa digunakan, namun metode-metode tersebut hanya untuk basisdata tradisional yang fokus kepada pencarian berbasis teks, sedangkan metode pencarian untuk gambar, suara atau video belum terlalu banyak dan masih dalam studi [2]. Beberapa penelitian terkait basisdata multimedia diantaranya MDID (Multiply Distorted Image Database) yang dilakukan oleh [3], basisdata wajah berdasarkan text-video [4], basisdata video multimodal biometric [5], basisdata UNMC-VIER yang berisi audio dan dan visual [6], basisdata GAID (Gait from Audio, Image and Depth) [7], basis data deteksi wajah KinectfaceDB [8].…”

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Analisis Seleksi Tingkat Kecocokan Gambar pada MDID Multimedia Database Dengan Menggunakan Metode ImageDNA

Moedjahedy

Zein²,

Isdayani³

et al. 2020

CogITo Smart Journal

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Dengan semakin tersedianya pilihan informasi digital saat ini, definisi multimedia yang umum diterima adalah kombinasi dari berbagai media seperti teks, gambar, suara, video, animasi. Dalam teoris basisdata, multimedia basisdata mulai dikenalkan yaitu kumpulan data multimedia terkait. Basisdata yang dipilih untuk optimasi dalam penelitian ini adalah MDID (Multiply Distorted Image Database) yang terdiri dari 20 gambar referensi dan 1600 gambar yang sudah diberikan distorsi. Basidata 1600 gambar tersebut akan diuji kecocokan dengan 20 gambar referensi dengan menggunakan metode ImageDNA. Nilai ImageDNA kemudian dilakukan uji data pencilan, sehingga gambar yang nilai ImageDNAnya ekstrim akan dikeluarkan dari basisdata MDID. Hasil dari penelitian ini adalah ada 100 gambar yang dikeluarkan

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An exhaustive study on data mining techniques in mining of Multimedia database

Cited by 4 publications

References 17 publications

A New Multimedia Web-Data Mining Approach based on Equivalence Class Evaluation Pipelined to Feature Maps onto Planar Projection

A New Multimedia Web-Data Mining Approach based on Equivalence Class Evaluation Pipelined to Feature Maps onto Planar Projection

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

Analisis Seleksi Tingkat Kecocokan Gambar pada MDID Multimedia Database Dengan Menggunakan Metode ImageDNA

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