Image Retrieval Using Data Mining and Image Processing Techniques

Chouhan, Preeti; Tiwari, Mukesh

doi:10.17148/ijireeice.2015.31212

Cited by 12 publications

(8 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We incorporate two of the most common types of noises encountered in transit photometry-a white Gaussian signal noise and an external noise due to random sunspots-in order to have an idea of how the folded transit method performs in actual space conditions. The white Gaussian noise simulates the background noise that may affect the instrument's performance and the natural noise from celestial sources [8,29]. On the other hand, the sunspots reduce the intensity of the host star depending on their activity and size.…”

Section: Folded Transit Light Curve With Noisementioning

confidence: 99%

Folded transit photometry

Manuel¹,

Hermosa²

2021

Preprint

View full text Add to dashboard Cite

Transit photometry is perhaps the most successful method for detecting exoplanets to date. However, a substantial amount of signal processing is needed since the dip in the signal detected, an indication that there is a planet in transit, is minuscule compared to the overall background signal due mainly to its host star. In this paper, we put forth a doable and straightforward method to enhance the signal and reduce noise. We discuss how to achieve higher planetary signals by subtracting equal halves of the host star -a folded detection. This results in a light curve with a double peak-to-peak signal, 2R 2 p /R 2 s , compared to the usual transit. We derive an expression of the light curve and investigate the effect of two common noises: the white Gaussian background noise and the noise due to the occurrences of sunspots. We show that in both simulation and analytical expression, the folded transit reduces the effective noise by a factor of 1/ √ 2. This reduction and the doubling of the signal enables: (1) less number of transit measurements to get a definitive transiting planet signal and (2) detection of smaller planetary radii with the usual transit with the same number of transit data. Furthermore, we show that in the presence of multiple sunspots, the estimation of planetary parameters is more accurate. While our calculations may be very simple, it covers the basic concept of planetary transits.

show abstract

Section: Folded Transit Light Curve With Noisementioning

confidence: 99%

Folded transit photometry

Manuel¹,

Hermosa²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The goals of data mining are fast retrieving of data or information, knowledge discovery from the databases, identification of hidden patterns and other patterns that are not explored before, reducing the level of complexity, saving processing time, and many other goals which are all useful in CBIR. Data mining is occasionally called Knowledge Discovery from Database (KDD) [4].…”

Section: Issn: 0067-2904mentioning

confidence: 99%

Image Retrieval Using Data Mining Technique

Abdul-Samad

Kamal

2020

eijs

View full text Add to dashboard Cite

Even though image retrieval is considered as one of the most important research areas in the last two decades, there is still room for improvement since it is still not satisfying for many users. Two of the major problems which need to be improved are the accuracy and the speed of the image retrieval system, in order to achieve user satisfaction and also to make the image retrieval system suitable for all platforms. In this work, the proposed retrieval system uses features with spatial information to analyze the visual content of the image. Then, the feature extraction process is followed by applying the fuzzy c-means (FCM) clustering algorithm to reduce the search space and speed up the retrieval process. The experimental results show that using the spatial features increases the system accuracy and that the clustering algorithm speeds up the image retrieval process. This shows that the proposed system works with texture and non-texture images.

show abstract

“…Preeti et al, [7] says, the Images to extract patterns and derive knowledge from large collections of images which mainly deals with identification and extraction of unique features for a particular domain. Increasing amount of illicit image data transmitted via the internet has triggered the need to develop effective image mining system for digital forensics purpose.…”

Section: IImentioning

confidence: 99%

A Survey on Feature Extraction Technique in Image Processing

Saravanakumar¹,

Hema²

2018

IJTSRD

View full text Add to dashboard Cite

Image processing is a method to convert an image into digital form and perform some operations on it. It is a type of signal dispensation in which input is image, video frame or photograph and output may be image or characteristics associated with that ima Image processing basically include the nthree steps as importing the image with optical scanner or by digital photography. Analyzing and manipulating the image which includes data compression and image enhancement and spotting pattern that are not to human eyes like satellite photographs. Output is the last stage in which result can be altered and report is based on image analysis. In recent trends, image processing domain plays a virtual part of real time application in modern world. Feature extraction from an initial set of measured data and builds derived values (features) intended to be informative and non redundant, facilitating the subsequent learning and generalization steps, some cases leading to better human interpretations.

show abstract

Image Retrieval Using Data Mining and Image Processing Techniques

Cited by 12 publications

References 3 publications

Folded transit photometry

Folded transit photometry

Image Retrieval Using Data Mining Technique

A Survey on Feature Extraction Technique in Image Processing

Contact Info

Product

Resources

About