Sparse Representation-Based Discriminative Metric Learning for Brain MRI Image Retrieval

Magnetic resonance imaging (MRI) can have a good diagnostic function for important organs and parts of the body. MRI technology has become a common and important disease detection technology. At the same time, medical imaging data is increasing at an explosive rate. Retrieving similar medical images from a huge database is of great significance to doctors’ auxiliary diagnosis and treatment. In this paper, combining the advantages of sparse representation and metric learning, a sparse representation-based discriminative metric learning (SRDML) approach is proposed for medical image retrieval of brain MRI. The SRDML approach uses a sparse representation framework to learn robust feature representation of brain MRI, and uses metric learning to project new features into the metric space with matching discrimination. In such a metric space, the optimal similarity measure is obtained by using the local constraints of atoms and the pairwise constraints of coding coefficients, so that the distance between similar images is less than the given threshold, and the distance between dissimilar images is greater than another given threshold. The experiments are designed and tested on the brain MRI dataset created by Chang. Experimental results show that the SRDML approach can obtain satisfactory retrieval performance and achieve accurate brain MRI image retrieval.

Large Scale Multimedia Management: A Comprehensive Review

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.

PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.

COLOR IMAGE RETRIEVAL BASED ON FUZZY NEURAL NETWORK AND SWARM INTELLIGENCE TECHNIQUES

Image retrieval is an important system for retrieving similar images by searching and browsing in a large database. The image retrieval system can be a reliable tool for people to optimize the use of image accumulation, and finding efficient methods to retrieve images is very important. Recent decades have marked increased research interest in field image retrieval. To retrieve the images, an important set of features is used. In this work, a combination of methods was used to examine all the images and detect images in a database according to a query image. Linear Discriminant Analysis (LDA) was used for feature extraction of the images into the dataset. The images in the database were processed by extracting their important and robust features and storing them in the feature store. Likewise, the strong features were extracted for specific query images. By using some Meta Heuristic algorithms such as Cuckoo Search (CS), Ant Colony Optimization (ACO), and using an artificial neural network such as single-layer Perceptron Neural Network (PNN), similarity was evaluated. It also proposed a new two method by hybridized PNN and CS with fuzzy logic to produce a new method called Fuzzy Single Layer Perceptron Neural Network (FPNN), and Fuzzy Cuckoo Search to examine the similarity between features for query images and features for images in the database. The efficiency of the system methods was evaluated by calculating the precision recall value of the results. The proposed method of FCS outperformed other methods such as (PNN), (ACO), (CS), and (FPNN) in terms of precision and image recall. ABSTRAK: Imej dapatan semula adalah sistem penting bagi mendapatkan imej serupa melalui carian imej dan melayari pangkalan besar data. Sistem dapatan semula imej ini boleh dijadikan alat boleh percaya untuk orang mengoptimum penggunaan pengumpulan imej, dan kaedah pencarian yang berkesan bagi mendapatkan imej adalah sangat penting. Beberapa dekad yang lalu telah menunjukan banyak penyelidikan dalam bidang imej dapatan semula. Bagi mendapatkan imej-imej ini, ciri-ciri set penting telah digunakan. Kajian ini menggunakan beberapa kaedah bagi memeriksa semua imej dan mengesan imej dalam pangkalan data berdasarkan imej carian. Kami menggunakan Analisis Diskriminan Linear (LDA) bagi mengekstrak ciri imej ke dalam set data. Imej-imej dalam pangkalan data diproses dengan mengekstrak ciri-ciri penting dan berkesan daripadanya dan menyimpannya dalam simpanan ciri. Begitu juga, ciri-ciri penting ini diekstrak bagi imej carian tertentu. Persamaan dinilai melalui beberapa algoritma Meta Heuristik seperti Carian Cuckoo (CS), Pengoptimuman Koloni Semut (ACO), dan menggunakan lapisan tunggal rangkaian neural buatan seperti Rangkaian Neural Perseptron (PNN). Dua cadangan baru dengan kombinasi hibrid PNN dan CS bersama logik kabur bagi menghasilkan kaedah baru yang disebut Lapisan Tunggal Kabur Rangkaian Neural Perceptron (FPNN), dan Carian Cuckoo Kabur bagi mengkaji persamaan antara ciri carian imej dan imej pangkalan data. Nilai kecekapan kaedah sistem dinilai dengan mengira ketepatan mengingat pada dapatan hasil. Kaedah FCS yang dicadangkan ini mengatasi kaedah lain seperti (PNN), (ACO), (CS) dan (FPNN) dari segi ketepatan dan ingatan imej.

CAPTCHA for crowdsourced image annotation: directions and efficiency analysis

Purpose Image annotation plays an important role in image retrieval process, especially when it comes to content-based image retrieval. In order to compensate the intrinsic weakness of machines in performing cognitive task of (human-like) image annotation, leveraging humans’ knowledge and abilities in the form of crowdsourcing-based annotation have gained momentum. Among various approaches for this purpose, an innovative one is integrating the annotation process into the CAPTCHA workflow. In this paper, the current state of the research works in the field and experimental efficiency analysis of this approach are investigated.Design/methodology/approach At first, and with the aim of presenting a current state report of research studies in the field, a comprehensive literature review is provided. Then, several experiments and statistical analyses are conducted to investigate how CAPTCHA-based image annotation is reliable, accurate and efficient.Findings In addition to study of current trends and best practices for CAPTCHA-based image annotation, the experimental results demonstrated that despite some intrinsic limitations on leveraging the CAPTCHA as a crowdsourcing platform, when the challenge, i.e. annotation task, is selected and designed appropriately, the efficiency of CAPTCHA-based image annotation can outperform traditional approaches. Nonetheless, there are several design considerations that should be taken into account when the CAPTCHA is used as an image annotation platform.Originality/value To the best of the authors’ knowledge, this is the first study to analyze different aspects of the titular topic through exploration of the literature and experimental investigation. Therefore, it is anticipated that the outcomes of this study can draw a roadmap for not only CAPTCHA-based image annotation but also CAPTCHA-mediated crowdsourcing and even image annotation.