Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

Varish, Naushad; Pal, Arup Kumar; Hassan, Rosilah; Hasan, Mohammad Kamrul; Khan, Altaf; Parveen, Nikhat; Banerjee, Debrup; Pellakuri, Vidyullatha; Haqis, Amin Ul; Memon, Imran

doi:10.1109/access.2020.3003911

Cited by 43 publications

(23 citation statements)

References 67 publications

(88 reference statements)

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“…Security implementation is a significant issue in the advanced world, cryptography calculations are one of the approaches to guarantee security [41]- [43]. Focusing on the picture encryption issue in clinical pictures of IoMT application in IoMT based structure [44].…”

Section: Security Measurement and Proposed Lightweight Encryption Algorithmmentioning

confidence: 99%

Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications

et al. 2021

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The importance of image security in the field of medical imaging is challenging. Several research works have been conducted to secure medical healthcare images. Encryption, not risking loss of data, is the right solution for image confidentiality. Due to data size limitations, redundancy, and capacity, traditional encryption techniques cannot be applied directly to e-health data, especially when patient data are transferred over the open channels. Therefore, patients may lose the privacy of data contents since images are different from the text because of their two particular factors of loss of data and confidentiality.Researchers have identified such security threats and have proposed several image encryption techniques to mitigate the security problem. However, the study has found that the existing proposed techniques still face application-specific several security problems. Therefore, this paper presents an efficient, lightweight encryption algorithm to develop a secure image encryption technique for the healthcare industry. The proposed lightweight encryption technique employs two permutation techniques to secure medical images. The proposed technique is analyzed, evaluated, and then compared to conventionally encrypted ones in security and execution time. Numerous test images have been used to determine the performance of the proposed algorithm. Several experiments show that the proposed algorithm for image cryptosystems provides better efficiency than conventional techniques.INDEX TERMS Internet of Medical Things, medical image encryption, lightweight encryption.

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Section: Security Measurement and Proposed Lightweight Encryption Algorithmmentioning

confidence: 99%

Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications

et al. 2021

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“…For the experiments and evaluation of our proposed method, the study uses two of the most known image datasets. These two datasets were used widely in many studies related to CBIR and their recent usage found in [26]. First dataset is Corel-1K [27] and has a total of 1000 images divided into ten categories with 100 images for each class.…”

Section: A Images Datasetsmentioning

confidence: 99%

Pre-trained CNNs Models for Content based Image Retrieval

Ahmed¹

2021

IJACSA

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Content based image retrieval (CBIR) systems is a common recent method for image retrieval and is based mainly on two pillars extracted features and similarity measures. Low level image presentations, based on colour, texture and shape properties are the most common feature extraction methods used by traditional CBIR systems. Since these traditional handcrafted features require good prior domain knowledge, inaccurate features used for this type of CBIR systems may widen the semantic gap and could lead to very poor performance retrieval results. Hence, features extraction methods, which are independent of domain knowledge and have automatic learning capabilities from input image are highly useful. Recently, pre-trained deep convolution neural networks (CNN) with transfer learning facilities have ability to generate and extract accurate and expressive features from image data. Unlike other types of deep CNN models which require huge amount of data and massive processing time for training purposes, the pre-trained CNN models have already trained for thousands of classes of large-scale data, including huge images and their information could be easily used and transferred. ResNet18 and SqueezeNet are successful and effective examples of pre-trained CNN models used recently in many machine learning applications, such as classification, clustering and object recognition. In this study, we have developed CBIR systems based on features extracted using ResNet18 and SqueezeNet pre-trained CNN models. Here, we have utilized these pre-trained CNN models to extract two groups of features that are stored separately and then later are used for online image searching and retrieval. Experimental results on two popular image datasets Core-1K and GHIM-10K show that ResNet18 features based on the CBIR method have overall accuracy of 95.5% and 93.9% for the two datasets, respectively, which greatly outperformed the traditional handcraft features based on the CBIR method.

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“…In this study, the Corel-1K (Li and Wang, 2008) and GHIM-10K (Liu et al, 2015) datasets were used. These two image datasets have been recently used in Varish et al (2020). The images in the first dataset were divided into ten different categories: Africans, Beaches, Buildings, Buses, Dinosaurs, Elephants, Flowers, Horses, Mountains, and Food.…”

Section: Image Datasetsmentioning

confidence: 99%

Implementation of early and late fusion methods for content-based image retrieval

Ahmed¹,

Mohamed²

2021

Int. j. adv. appl. sci.

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Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

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Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

Cited by 43 publications

References 67 publications

Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications

Lightweight Encryption Technique to Enhance Medical Image Security on Internet of Medical Things Applications

Pre-trained CNNs Models for Content based Image Retrieval

Implementation of early and late fusion methods for content-based image retrieval

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