Fast Content Based Color Image Retrieval System Based on Texture Analysis of Edge Map

Salehian, Hesamoddin; Zamani, Fatemeh; Jamzad, Mansour

doi:10.4028/www.scientific.net/amr.341-342.168

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…After dividing these numbers to the total number of pixels, we get 12x4 = 48 real numbers. In the end color and texture feature vectors are concatenated and a 66 length feature vector for every image is calculated [8]. Fig.4 shows the feature extraction process.…”

Section: Resultsmentioning

confidence: 99%

Color and Texture Feature Extraction Using Apache Hadoop Framework

Sabarad

Kankudti

Meena

et al. 2015

2015 International Conference on Computing Communication Control and Automation

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Multimedia data is expanding exponentially. The rapid growth of technology combined with affordable storage and capabilities has lead to explosion in the availability and applications of multimedia. Most of the data is available in the form of images and videos. Today large amount of image data is produced through digital cameras, mobile phones and other sources. Processing of this large collection of images involve highly complex and repetitive operations on a large database leading to challenges of optimizing the query time and data storage capacity. Many image processing and computer vision algorithms are applicable to large-scale data tasks. It is often desirable to run the image processing algorithms on large data sets (e.g. larger than 1 TB) that are currently limited by the computational power of a single computer system. In order to handle such a huge data, we propose execution of time and space intensive computer vision algorithms on a distributed computing platform by using Apache Hadoop framework. Basically, Hadoop framework works based on divide and conquer strategy. The task of extracting color and texture features will be divided and assigned to multiple nodes of the Hadoop cluster. A significant speedup in computation time and efficient utilizations of memory can be achieved by exploiting the parallelism nature of Apache Hadoop framework. The Most important advantage of using Hadoop is, it is highly economical as whole framework can be implemented on existing commodity machines. Moreover, the system is highly fault tolerant and less vulnerable to node failures.

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Section: Resultsmentioning

confidence: 99%

Color and Texture Feature Extraction Using Apache Hadoop Framework

Sabarad

Kankudti

Meena

et al. 2015

2015 International Conference on Computing Communication Control and Automation

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“…Despite its benefits, color correlograms are affected by scaling and lighting changes" [52]. The color moment is one among the techniques to extract the color feature from the image using statistical values: mean, standard deviation, and skewness [53].…”

Section: Related Workmentioning

confidence: 99%

“…Every image in the repository acts as a query image in our tests. The accuracy P(N) and recall R(N) for fetching the top N items, as defined in [52,53], are used to evaluate an image retrieval system's efficiency. ;…”

Section: Performance and Measuresmentioning

confidence: 99%

Color Texture Description with Diagonal Local Binary Patterns Using a New Distance Metric for Impressive Image Retrieval

Sajwan¹,

Ranjan²,

Swapnil³

2021

INDJCSE

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With the reliability and dependence of the world over the digitalization of information in all spectrums of professional and personal life, the volume of digital data is increasing exponentially. With such a huge database, retrieving information, and images from the storage media pose serious challenges. In this paper, a diagonal local binary pattern for color images (DLBPC) and a new distance metric are proposed for the retrieval of data with high accuracy and speed. The descriptor employs the deviceindependent L*a*b* color space. The discriminative quality of DLBPC is enhanced by combining local features with the (color moment) global feature of the image for effective image retrieval. To assess the efficacy, the suggested system was implemented on challenging datasets: Wang -1K and GHIM-10K. The analysis findings demonstrate the efficacy of the suggested system over other reported systems.

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“…The most common image features used are color, texture, and shape. Color can be determined directly by using a color histogram [21] which shows pixel distribution of each color within the image. Statistical features can be extracted either from a color-or gray-scale histogram [22].…”

Section: Features Typesmentioning

confidence: 99%

Generating Mosaic Images Based On Texture Analysis

Taqa¹

2018

J.Edu.Sci.

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Photo-mosaic (mosaic-image) generation is the process of dividing an input image into equal rectangular blocks (image blocks), each of which is replaced with another image (tile image) that matches the features of a corresponding image block. When the produced photo mosaic is seen from a distance, it seemingly forms the input image.Photo mosaic is not a new concept; however, only a few publications on this subject are available because of its commercial nature. The quality of the produced photo mosaic depends on the size of the database and the variety of images. In addition to process time, a bottleneck occurs because of the discrepancy in the match rate between the input image and the produced photo mosaic.This research proposes three intelligence-based approaches for producing photo mosaics in less time: (1) k-means clustering with Manhattan distance, (2) back propagation neural network with Manhattan distance, and (3) hybrid fuzzy logic with Elman neural network.Three groups of features are extracted and then used to find the best matching tile image for each corresponding image block within the container image. The first group comprises statistical features extracted from a 64-gray level quantized histogram. These features are variance, mean, skewness, kurtosis, and energy. The second group is a subset of Tamura features, namely, coarseness, contrast, and directionality. The last group includes a feature called edge rate, which is computed as the percentage of edge pixels within an image that can be detected using a Canny filter. In addition, two methods of color correction are used to adjust the colors of a tile image to match the colors of a corresponding image block. The first method is based on mean, whereas the second is based on histogram specification.Finally, experimental results show that hybrid fuzzy logic with Elman neural network is the best among the three approaches used. This technique needs 10.0 seconds to produce photo mosaics, with a correlation rate of 0.82 between the container image and the produced photo mosaics,

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Fast Content Based Color Image Retrieval System Based on Texture Analysis of Edge Map

Cited by 6 publications

References 10 publications

Color and Texture Feature Extraction Using Apache Hadoop Framework

Color and Texture Feature Extraction Using Apache Hadoop Framework

Color Texture Description with Diagonal Local Binary Patterns Using a New Distance Metric for Impressive Image Retrieval

Generating Mosaic Images Based On Texture Analysis

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