scholarly journals Retrieval of Context and Content based Images using Enhanced Crow Search Optimization

2019 ◽  
Vol 8 (6) ◽  
pp. 2990-2995
Keyword(s):  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
Computational Time ◽  
Fuzzy C Means ◽  
Search Optimization

In late decades, Content-Based Image Retrieval (CBIR) has been one on the most distinctive research zones in the field of Computer applications. The critical goal of this examination is to improve the recovering presentation of CBIR framework by fusing advancement strategies to foresee suitable centroid in Fuzzy C-Means (FCM).The expectation of consolidating streamlining method to anticipate FCM centroids positively decrease intricacy and computational time. The outcomes clear that consolidation of ECSO with FCM uncovers better outcomes over challenge procedures when compared with existing procedures like PWO, SSO and CSO.

Enhanced Content-Based Image Retrieval Using Information Oriented Angle-Based Local Tri-Directional Weber Patterns

2021 ◽  
pp. 2150046
Author(s):  
Gangavarapu Venkata Satya Kumar ◽  
Pillutla Gopala Krishna Mohan
Keyword(s):  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
The Novel ◽  
Image Content ◽  
Query Image ◽  
Proposed Model ◽  
Maximum Mutual Information ◽  
Basic Features

In diverse computer applications, the analysis of image content plays a key role. This image content might be either textual (like text appearing in the images) or visual (like shape, color, texture). These two image contents consist of image’s basic features and therefore turn out to be as the major advantage for any of the implementation. Many of the art models are based on the visual search or annotated text for Content-Based Image Retrieval (CBIR) models. There is more demand toward multitasking, a new method needs to be introduced with the combination of both textual and visual features. This paper plans to develop the intelligent CBIR system for the collection of different benchmark texture datasets. Here, a new descriptor named Information Oriented Angle-based Local Tri-directional Weber Patterns (IOA-LTriWPs) is adopted. The pattern is operated not only based on tri-direction and eight neighborhood pixels but also based on four angles [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]. Once the patterns concerning tri-direction, eight neighborhood pixels, and four angles are taken, the best patterns are selected based on maximum mutual information. Moreover, the histogram computation of the patterns provides the final feature vector, from which the new weighted feature extraction is performed. As a new contribution, the novel weight function is optimized by the Improved MVO on random basis (IMVO-RB), in such a way that the precision and recall of the retrieved image is high. Further, the proposed model has used the logarithmic similarity called Mean Square Logarithmic Error (MSLE) between the features of the query image and trained images for retrieving the concerned images. The analyses on diverse texture image datasets have validated the accuracy and efficiency of the developed pattern over existing.

A Novel Image Encryption with Deep Learning Model for Secure Content based Image Retrieval

2019 ◽  
pp. 54-64
Author(s):  
Mohamed Elsharkawy ◽  
◽  
Ahmed N. Al Masri ◽  
◽  
Keyword(s):  
Deep Learning ◽  
Image Retrieval ◽  
Image Encryption ◽  
Cuckoo Search ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Search Optimization ◽  
Proposed Model ◽  
Cuckoo Search Optimization ◽  
Visual Properties

From the last decades, a massive quantity of images gets generated and continues to rise to a maximum extent in the forthcoming data. The process of retrieving images based on a query image (QI) is a proficient method of accessing the visual properties from large datasets. Content-based image retrieval (CBIR) provides a way of effectively retrieving images from large databases. At the same time, image encryption techniques can be integrated into the CBIR model to retrieve the images securely. Therefore, this paper presents new image encryption with a deep learning-based secure CBIR model called IEDL-SCBIR. The proposed IEDL-SCBIR technique intends to encrypt the images as well as securely retrieve them. The proposed IEDL-SCBIR technique follows a two-stage process: optimal elliptic curve cryptography (ECC) based encryption and DL based image retrieval. The proposed model derives a cuckoo search optimization (CSO) with the ECC technique for the image encryption process in which the CSO algorithm is applied for optimal key generation. In addition, VGG based feature extraction with Euclidean distance-based similarity measurement is applied for the retrieval process. To validate the enhanced performance of the IEDL-SCBIR technique, a comprehensive results analysis takes place, and the obtained results demonstrate the betterment over the other methods.

scholarly journals Content Based Image Retrieval using Transform Domain Features and Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 3008-3011
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Integer Wavelet ◽  
Mean Variance ◽  
Efficient Recognition

In this paper, Content Based Image Retrieval using Transform domain features and algorithms has been implemented. The image can be decomposed by Discrete Wavelet Transform (DWT) to extract the features based on DC coefficients. Each sub-image is calculated by mean, variance and standard deviation to get more efficient recognition. The database image also applied in the domain of Stationary Wavelet Transform (SWT) and Integer Wavelet Transform (IWT) by using different distance measures. The proposed algorithm is the combination of DWT, SWT and IWT has been implemented using COREL database. This proposed method has more efficient recognition and less computational time over existing methods

A Content Based Image Retrieval Method Based on K-Means Clustering Technique

2018 ◽  
Vol 16 (1) ◽  
pp. 82-96 ◽  
Author(s):  
Mohamed Ouhda ◽  
Khalid El Asnaoui ◽  
Mohammed Ouanan ◽  
Brahim Aksasse
Keyword(s):  
Image Retrieval ◽  
Accurate Result ◽  
Efficient Algorithms ◽  
Image Databases ◽  
New Method ◽  
Content Based Image Retrieval ◽  
Computational Time ◽  
Retrieval Method ◽  
Clustering Technique ◽  
Similar Images

With the appearance of many devices that are used in image acquisition comes a large number of images every day. The rapid access to these huge collections of images and retrieval of similar images (Query) from this huge collection of images presents major challenges and requires efficient algorithms. The main goal of the proposed system is to provide an accurate result with lower computational time. For this purpose, the authors apply a new method based on k-means clustering technique to match image's descriptors. This article provides a detailed view of the solution the authors have adopted and which perfectly meets their needs. For validation, they apply all of these techniques on two image databases in order to evaluate the performance of their system.

scholarly journals Perbandingan Metode Segmentasi SOM dan Fuzzy CMeans pada Content-Based Image Retrieval Berbasis Warna

2018 ◽  
Vol 17 (3) ◽  
pp. 333
Author(s):  
I Gusti Ngurah Winanda Wijaksana ◽  
Ida Ayu Dwi Giriantari ◽  
I Made Sudarma
Keyword(s):  
Image Retrieval ◽  
Content Based Image Retrieval ◽  
Fuzzy C Means ◽  
Color Moment

Intisari— Sulitnya menentukan kata kunci yang tepat untuk mendapatkan citra yang diinginkan merupakan kelemahan pencarian citra berdasarkan kata kunci metadata. Perkembangan teknologi saat ini mengarah pada pencarian citra berdasarkan konten atau Content-based Image Retrieval (CBIR). Salah satu ciri konten citra yang digunakan untuk temu kembali citra adalah ciri warna. Untuk semakin meningkatkan kinerja CBIR, pada penelitian ini diteliti mengenai perbandingan metode segmentasi SOM dan Fuzzy C-Means. Metode segmentasi ini memisahkan foreground dan background dari citra query untuk mendapatkan kinerja CBIR yang lebih baik. Adapun database citra yang digunakan adalah Wang Dataset. Pengujian dilakukan dengan citra uji yang telah mengalami perubahan skala, rotasi dan kekaburan. Hasil dari pengujian menunjukkan penggunaan metode segmentasi meningkatkan nilai recall atau citra benar yang berhasil ditemukan, namun secara signifikan mengurangi nilai precision atau rasio citra benar dari keseluruhan citra yang ditemukan dibandingkan tanpa mengunakan metode segmentasi.   Kata Kunci— CBIR, Color Moment, SOM, FCM.

scholarly journals LApIS: developing research applied to health care area

2011 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Fátima L S Nunes ◽  
Helton H Bíscaro ◽  
Márcio E Delamaro ◽  
Romero Tori ◽  
Ricardo Nakamura
Keyword(s):  
Image Processing ◽  
Health Care ◽  
Virtual Reality ◽  
Image Retrieval ◽  
Computer Applications ◽  
Content Based Image Retrieval ◽  
History Of ◽  
Application Scope

The Laboratory of Computer Applications for Health Care is a Brazilian lab researching in the virtual reality, Content-based image retrieval, and image processing areas, mainly developing applications to the health care area, although many of the techniques and tools extrapolate this application scope. . In this paper we present a brief history of the laboratory, its mission as well as current projects and collaborations. All the projects developed at LApIS have some relevant aspects for researches in the graphics area, which can become new opportunities for student’s integration and new collaborations.

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