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Proceedings ArticleDOI

Color Image Retrieval Using DFT Phase Information

TLDR
DFT transform based approach for image retrieval using Image classes is state, which retrieves images based on the feature vector values of DFT Phase information of RGB's planes with similar to that of Feature vector of Image class.
Abstract
With the advancement of Image acquisition and storing, image retrieval has been proven as the research problem. Many approaches for image retrieval, has been stated by researchers to solve the image retrieval problem. In this paper we state DFT transform based approach for image retrieval using Image classes. Here formation of Feature vectors of the Images is based on Color based DFT Phase information of images those belongs to same class. DFT Image transform provides effective way to differentiate the image textures. Particularly Phase part of DFT carries the important information about the objects in image. In the proposed approach of image retrieval, DFT phase information is used for representing the images using feature vector effectively. To make image retrieval more accurate, class wise images are considered for creation of database feature vectors. As, images belonging same class are content wise similar, the generalized feature vector is produced for each class Generalized feature vectors represents all images of that class. Cosine correlation similarity measure is used in the proposed approach. 4 Different types of feature vectors are created and tested for each image class. The Images are retrieved based on the feature vector values of DFT Phase information of RGB's planes with similar to that of Feature vector of Image class. Image retrieval Performance of the proposed approach is compared for database of 1000 images of 10 different categories. Average Accuracy of Image retrieval is above 60% for all classes and more than 75% for some of the image classes.

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Citations
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Book ChapterDOI

Introduction to cognitive computing and its various applications

TL;DR: Cognitive computing is an intelligent system that converses with and mimics the human being in a natural form by learning at scale, reasoning with purpose, and adaptive, interactive, iterative, and stateful and contextual in commercial and widespread applications.
Proceedings ArticleDOI

Analysis of Defect Classification Approaches for Fabric Images based on Four DFT Sector Features

TL;DR: It has been observed that Flower and Diamond pattern fabric materials have closer classification rates after applying SVM, Grid Search, and Random Forest algorithms, and oil stains and punches, these defects are found to be have an average classification rates similar to each other across all fabric types and classification methods.
Proceedings ArticleDOI

Defect Classification for Silk Fabric Based on Four DFT Sector Features

TL;DR: It has been observed that the Random Forest is most efficient algorithm for defect classification for silk fabric due to its high rate of classification.
References
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Journal ArticleDOI

Content based image retrieval using motif cooccurrence matrix

TL;DR: The retrieval using the MCM is better than the CCM since it captures the third order image statistics in the local neighborhood and the use of MCM considerably improves the retrieval performance.
Journal ArticleDOI

Colour image retrieval based on primitives of colour moments

TL;DR: A colour image retrieval method based on the primitives of colour moments is proposed and a relevance feedback algorithm is provided to automatically determine the best method according to the user's response.
Dissertation

Image Retrieval Based on Content Using Color Feature

TL;DR: This paper presents a CBIR system that uses Ranklet Transform and the color feature as a visual feature to represent the images and to speed up the retrieval time.

CBIR using Upper Six FFT Sectors of Color Images for Feature Vector Generation

TL;DR: This paper is using Fast Fourier Transform to generate the feature vector which considers the mean real and mean imaginary parts of complex numbers of polar coordinates in frequency domain and uses 36 mean values of real and imaginary parts in total.
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