scholarly journals Cloud-Based Image Retrieval Using GPU Platforms

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 48 ◽  
Author(s):  
Sidi Ahmed Mahmoudi ◽  
Mohammed Amin Belarbi ◽  
El Wardani Dadi ◽  
Saïd Mahmoudi ◽  
Mohammed Benjelloun
Keyword(s):  
Image Retrieval ◽  
Performance Improvement ◽  
Computation Time ◽  
Image Features ◽  
Multimedia Retrieval ◽  
Large Database ◽  
Computation Complexity ◽  
Multimedia Documents ◽  
Large Databases ◽  
Reduction Of Energy Consumption

The process of image retrieval presents an interesting tool for different domains related to computer vision such as multimedia retrieval, pattern recognition, medical imaging, video surveillance and movements analysis. Visual characteristics of images such as color, texture and shape are used to identify the content of images. However, the retrieving process becomes very challenging due to the hard management of large databases in terms of storage, computation complexity, temporal performance and similarity representation. In this paper, we propose a cloud-based platform in which we integrate several features extraction algorithms used for content-based image retrieval (CBIR) systems. Moreover, we propose an efficient combination of SIFT and SURF descriptors that allowed to extract and match image features and hence improve the process of image retrieval. The proposed algorithms have been implemented on the CPU and also adapted to fully exploit the power of GPUs. Our platform is presented with a responsive web solution that offers for users the possibility to exploit, test and evaluate image retrieval methods. The platform offers to users a simple-to-use access for different algorithms such as SIFT, SURF descriptors without the need to setup the environment or install anything while spending minimal efforts on preprocessing and configuring. On the other hand, our cloud-based CPU and GPU implementations are scalable, which means that they can be used even with large database of multimedia documents. The obtained results showed: 1. Precision improvement in terms of recall and precision; 2. Performance improvement in terms of computation time as a result of exploiting GPUs in parallel; 3. Reduction of energy consumption.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 281-302
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

A Content-Based Approach to Medical Image Database Retrieval

2009 ◽  
pp. 1062-1083
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

A Content-Based Approach to Medical Image Database Retrieval

2011 ◽  
pp. 258-292 ◽  
Author(s):  
Chia-Hung Wei ◽  
Chang-Tsun Li ◽  
Roland Wilson
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Database Retrieval ◽  
Retrieval Systems ◽  
Large Databases ◽  
Advance Research ◽  
Intervention Content

Content-based image retrieval (CBIR) makes use of image features, such as color and texture, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. This chapter introduces a content-based approach to medical image retrieval. Fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. A case study, which describes the methodology of a CBIR system for retrieving digital mammogram database, is then presented. This chapter is intended to disseminate the knowledge of the CBIR approach to the applications of medical image management and to attract greater interest from various research communities to rapidly advance research in this field.

scholarly journals Large Scale Multimedia Management: A Comprehensive Review

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 28
Author(s):  
Saïd Mahmoudi ◽  
Mohammed Amin Belarbi
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Large Datasets ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Easy Access ◽  
3D Images ◽  
Comprehensive Review ◽  
Large Databases ◽  
Multimedia Management

Multimedia applications deal, in most cases, with an extremely high volume of multimedia data (2D and 3D images, sounds, videos). That is why efficient algorithms should be developed to analyze and process these large datasets. On the other hand, multimedia management is based on efficient representation of knowledge which allows efficient data processing and retrieval. The main challenge in this era is to achieve clever and quick access to these huge datasets to allow easy access to the data and in a reasonable time. In this context, large-scale image retrieval is a fundamental task. Many methods have been developed in the literature to achieve fast and efficient navigating in large databases by using the famous content-based image retrieval (CBIR) methods associated with these methods allowing a decrease in the computing time, such as dimensional reduction and hashing methods. More recently, these methods based on convolutional neural networks (CNNs) for feature extraction and image classification are widely used. In this paper, we present a comprehensive review of recent multimedia retrieval methods and algorithms applied to large datasets of 2D/3D images and videos. This editorial paper discusses the mains challenges of multimedia retrieval in a context of large databases.

scholarly journals Unsupervised Deep Feature Learning for Remote Sensing Image Retrieval

2018 ◽  
Vol 10 (8) ◽  
pp. 1243 ◽  
Author(s):  
Xu Tang ◽  
Xiangrong Zhang ◽  
Fang Liu ◽  
Licheng Jiao
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Feature Learning ◽  
Remote Sensing Image ◽  
Code Word ◽  
Image Features ◽  
Learning Method ◽  
L1 Norm ◽  
Image Descriptor ◽  
Image Archives

Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm. Thus, we name the obtained feature deep BOW (DBOW). The learning process consists of two parts, including image descriptor learning and feature construction. First, to explore the complex contents within the RS image, we extract the image descriptor in the image patch level rather than the whole image. In addition, instead of using the handcrafted feature to describe the patches, we propose the deep convolutional auto-encoder (DCAE) model to deeply learn the discriminative descriptor for the RS image. Second, the k-means algorithm is selected to generate the codebook using the obtained deep descriptors. Then, the final histogrammic DBOW features are acquired by counting the frequency of the single code word. When we get the DBOW features from the RS images, the similarities between RS images are measured using L1-norm distance. Then, the retrieval results can be acquired according to the similarity order. The encouraging experimental results counted on four public RS image archives demonstrate that our DBOW feature is effective for the RSIR task. Compared with the existing RS image features, our DBOW can achieve improved behavior on RSIR.

scholarly journals Statistical Model Based Digital Image Retrieval and Fragile Watermarking

2021 ◽  
Author(s):  
Hua Yuan
Keyword(s):  
Image Retrieval ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Statistical Modelling ◽  
Gaussian Mixture ◽  
Image Features ◽  
Fragile Watermarking ◽  
Model Parameters ◽  
Wavelet Domain ◽  
Watermarking Scheme

The objective of this thesis is to acquire abstract image features through statistical modelling in the wavelet domain and then based on the extracted image features, develop an effective content-based image retreival (CBIR) system and a fragile watermarking scheme. In this thesis, we first present a statistical modelling of images in the wavelet domain through a Gaussian mixture model (GMM) and a generalized Gaussian mixture model (GGMM). An Expectation Maximization (EM) algorithm is developed to help estimate the model parameters. A novel similarity measure based on the Kullback-Leibler divergence is also developed to calculate the distance of two distinct model distributions. We then apply the statistical modelling to two application areas: image retrieval and fragile watermarking. In image retrieval, the model parameters are employed as image features to compose the indexing feature space, while the feature distance of two compared images is computed using the novel similarity measure. The new image retrieval method has a better retrieval performance than most conventional methods. In fragile watermarking, the model parameters are utilized for the watermark embedding. The new watermarking scheme achieves a virtually imperceptible embedding of watermarks because it modifies only a few image data and embeds watermarks at image texture edges. A multiscale embedding of fragile watermarks is given to enhance the embeddability rate and on the other hand, to constitute a semi-fragile approach.

scholarly journals Performance Evaluation of Gradient Domain and Pyramid Blending Used in Image Stitching Process with ORB Binary Descriptor

2019 ◽  
Vol 9 (1) ◽  
pp. 1849-1854
Keyword(s):  
Feature Detection ◽  
Computation Time ◽  
Automatic Target Recognition ◽  
Image Features ◽  
Image Feature ◽  
Image Stitching ◽  
Imaging Data ◽  
Multiple Images ◽  
Binary Descriptor ◽  
Blending Method

Panorama development is the basically method of integrating multiple images captured of the same scene under consideration to get high resolution image. This process is useful for combining multiple images which are overlapped to obtain larger image. Usefulness of Image stitching is found in the field related to medical imaging, data from satellites, computer vision and automatic target recognition in military applications. The goal objective of this research paper is basically for developing an high improved resolution and its quality panorama having with high accuracy and minimum computation time. Initially we compared different image feature detectors and tested SIFT, SURF, ORB to find out the rate of detection of the corrected available key points along with processing time. Later on, testing is done with some common techniques of image blending or fusion for improving the mosaicing quality process. In this experimental results, it has been found out that ORB image feature detection and description algorithm is more accurate, fastest which gives a higher performance and Pyramid blending method gives the better stitching quality. Lastly panorama is developed based on combination of ORB binary descriptor method for finding out image features and pyramid blending method.

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

2021 ◽  
Vol 8 (7) ◽  
pp. 97-105
Author(s):  
Ali Ahmed ◽  
◽  
Sara Mohamed ◽  
Keyword(s):  
Image Retrieval ◽  
Texture Features ◽  
Image Features ◽  
Distance Measures ◽  
Content Based Image Retrieval ◽  
Late Fusion ◽  
Query Image ◽  
Fusion Methods ◽  
And Performance ◽  
Image Repository

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.

Evaluation of Light Inspired Optimization Algorithm-Based Image Retrieval

2014 ◽  
Vol 573 ◽  
pp. 529-536
Author(s):  
T. Kanimozhi ◽  
K. Latha
Keyword(s):  
Image Retrieval ◽  
Firefly Algorithm ◽  
Retrieval System ◽  
Computation Time ◽  
Image Databases ◽  
Image Search ◽  
Retrieval Process ◽  
Large Collection ◽  
Image Retrieval System ◽  
Unique Method

Image retrieval system becoming a more popular in all the disciplines of image search. In real-time, interactive image retrieval system has become more accurate, fast and scalable to large collection of image databases. This paper presents a unique method for an image retrieval system based on firefly algorithm, which improve the accuracy and computation time of the image retrieval system. The firefly algorithm is utilized to optimize the image retrieval process via search for nearly optimal combinations between the corresponding features as well as finding out approximate optimized weights for similarities with respect to the features. The proposed method is able to dynamically reflect the user’s intention in the retrieval process by optimizing the objective function. The Efficiency of the proposed method is compared with other existing image retrieval methods through precision and recall. The performance of the method is experimented on the Corel and Caltech database images.

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