Image Retrieval System by Skin Color and Edge Information

Thangamani, M.; Priya, P Seetha Subha

doi:10.18831/djcse.in/2015011003

Cited by 4 publications

(5 citation statements)

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“…The Colour Index Local Auto-Correlations (CILAC) method which is a vast sub-division of ACC was adopted to extract more specific colour spacing of the images in detail which proved to be a robust framework encompassing the minute filtering process on the images as an initiative part of pre-processing the image, when compared to other such colour feature extraction techniques [12]. It was by the use of Nearest Neighbour algorithm after calculating the quality of the image, the filtering of the image was successfully performed [13].…”

Section: Current Approachmentioning

confidence: 99%

Identifying Image Falsification by Enhanced Auto Colour Correlation Approach – A Forgery Forensic

Medikonda¹,

Janarthanan²

2017

DJ JAECE

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As images are increasingly being used today as evidences for criminal justification, the forgery on the images that are used for criminal investigations can cause a great threat in the implementation of truth and justice. The innovations in digitizing technology along with image editing software have made it very easy to tamper digital images. So, there is a need for examining the authenticity of the images. Digital image forensics has emerged to solve the problems behind this conflicting issue. In this work, the test images are pre-processed by means of blur-index calculation and false colour removal with the help of nearest neighbour algorithm. Next the colour feature extraction process is carried out. This is done by generating the histogram of the image, and feeding it as input to the Colour Index Local Auto-Correlations (CILAC) model, to extract the colour index. This facilitates the enhanced Auto Colour Correlation (ACC) approach. Next the image undergoes 8Z affine transformation and the images are matched to obtain the result. Finally, a comparison of the results obtained with the other existing methods is done which reveals that the ACC enhanced approach has a better performance in terms of precision, recall and F1 score.

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Section: Current Approachmentioning

confidence: 99%

Identifying Image Falsification by Enhanced Auto Colour Correlation Approach – A Forgery Forensic

Medikonda¹,

Janarthanan²

2017

DJ JAECE

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“…In [5,6,7] artificial neural networks are employed to detect spam. Here perceptron learning rule, is used to design the artificial neural network which means that a stochastic gradient method is used for training and the true gradient is analysed on a sigle training example.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Automated Spam Filtering through Data Mining Approach

Mallampati¹,

Madhur²,

Abhinay³

et al. 2017

SIJST

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Spam messages can be referred as those mails which come into act in the absence of a standard agreement among the senders and receivers for receiving e-mail solicitation. Usually these messages are sent in bulk quantities. For preventing the spam delivery, an automatic system based spam filter tool is employed. The objectives of spam filters and spam are contradicted diametrically. A spam filter can be termed effective if it recognizes spam. On the other hand, it is ineffective when it escapes the filters. It is the need of the hour that these bulk unsolicited e-mails be effectively filtered. Increasing volume of these mails emphasizes on the requirement and design of dependable anti-spam filters. One of the techniques which is used widely to filter these spam e-mails is the machine learning technique. They possess in built algorithms which filters spam e-mails at commendable rates. In this project we present a method, to access classifier security against their attacks profoundly concentrating on the content of the message. The dependence on a predefined set of keywords is reduced. The paper also focuses on related works which apply machine learning techniques using naïve Bayes classification for e-mail message classification.

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“…[16,17] The input of the thersholding function is a gray scale image which is derived from the above step. The output is a binary image representing segmentation.…”

Section: Thresholdingmentioning

confidence: 99%

“…These methods have certain restrictions and the results are not produced efficiently. [15,16,17] The shadow detection and compensation process were done by using Dual-Pass Otsu Method. The pixel value is separated in to high and low level intensities.…”

Section: Introductionmentioning

confidence: 99%

A Framework in Shadow Detection and Compensation of Images

Kiran¹

2016

DJ JAECE

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Shadow detection and removal is a crucial and unavoidable task in computer vision algorithms. In this paper detection and reconstruction processes are employed separately on an image. In the detection process, the shadow and non-shadow parts of an image are detected separately using thresholding, histogram equalization and shadow masking. The reconstruction process can be split into two steps. The first step is the calculation of mean and standard deviation for the shadow and nonshadow parts and the second step is finding the difference between the shadow and non-shadow parts and applying these differences to the shadow part. The result is a quality image with non-shadow region.

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Image Retrieval System by Skin Color and Edge Information

Cited by 4 publications

References 0 publications

Identifying Image Falsification by Enhanced Auto Colour Correlation Approach – A Forgery Forensic

Identifying Image Falsification by Enhanced Auto Colour Correlation Approach – A Forgery Forensic

Automated Spam Filtering through Data Mining Approach

A Framework in Shadow Detection and Compensation of Images

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