Content-based Image Retrieval Techniques Research Articles

Lung cancer prediction and retrieval using multistage hybrid prediction approach

In cancer diagnosis computer-aided Prediction is considered as significant research domain. The generation and processing of lung cancer identification and Prediction is expensive. During past decades, content-based image retrieval (CBIR) technique has been applied in various medical applications. For effective diagnosis of lung cancer radiologist’s requires effective approach for cancer prediction and diagnosis. In this research, for prediction of CT lung images is achieved using hybrid Prediction approach is adopted with integration of logistic regression and Adaboost classifier. Feature extraction and retrieval of lung cancer image applied through optimization technique known as firefly approach for processing several vector features of lung images to derive salient characteristics for achieving best features. Results illustrated that proposed prediction and retrieval approach exhibits significant performance rather than conventional technique. The proposed approach provides classification accuracy of 97% which is significantly higher.

MSPPIR: Multi-Source Privacy-Preserving Image Retrieval in cloud computing

Content-Based Image Retrieval (CBIR) techniques have been widely researched and in service with cloud computing like Google Images. However, the images always contain rich sensitive information. In this case, privacy protection becomes a big problem as the cloud always cannot be fully trusted. Many privacy-preserving image retrieval schemes have been proposed, in which the image owner can upload the encrypted images to the cloud, and the owner himself or the authorized user can execute the secure retrieval with the help of the cloud. Nevertheless, few existing researchers notice the multi-source scene that is more practical. In this paper, we analyze the difficulties in Multi-Source Privacy-Preserving Image Retrieval (MSPPIR). Then we use the image in JPEG-format as the example, to propose a scheme called JES-MSIR, namely a novel JPEG image Encryption Scheme which is made for Multi-Source content-based Image Retrieval. JES-MSIR can support the requirements of MSPPIR, including the constant-rounds secure retrieval from multiple sources and the union of multiple sources for better retrieval services. Experiment results and security analysis on the proposed scheme show its efficiency, security, and accuracy.

A bipartite graph approach to retrieve similar 3D models with different resolution and types of cardiomyopathies

Three-dimensional (3D) model retrieval uses content-based image retrieval (CBIR) techniques to search for the most similar 3D objects in a dataset, usually considering their geometry and organization in a feature vector. Feature vectors from different objects were compared to establish their similarities. Although this type of comparison typically uses metric distances, such metrics present limitations when the vector lengths are different. Signal-based descriptors are a promising approach for extracting features from 3D objects, but they generate feature vectors with different lengths. Thus, new methods for measuring the similarity are required. This study proposes an approach to 3D model retrieval as a network flow problem using bipartite graphs. The approach was applied to support the diagnosis of cardiomyopathies, considering 3D objects reconstructed from cardiac images of the left ventricle. We achieved an AUC value of 0.93 under the best retrieval scenario. The results also indicate that modeling a 3D model retrieval technique as a network flow problem using graphs can provide a promising manner to compare 3D objects with different shapes and sizes. This strategy, coupled with personal patient data, achieves better results than methods using classical comparison approaches.

BOEW: A Content-Based Image Retrieval Scheme Using Bag-of-Encrypted-Words in Cloud Computing

Content-based Image Retrieval (CBIR) techniques have been extensively studied with the rapid growth of digital images. Generally, CBIR service is quite expensive in computational and storage resources. Thus, it is a good choice to outsource CBIR service to the cloud server that is equipped with enormous resources. However, the privacy protection becomes a big problem, as the cloud server cannot be fully trusted. In this paper, we propose an outsourced CBIR scheme based on a novel bag-of-encrypted-words (BOEW) model. The image is encrypted by color value substitution, block permutation, and intra-block pixel permutation. Then, the local histograms are calculated from the encrypted image blocks by the cloud server. All the local histograms are clustered together, and the cluster centers are used as the encrypted visual words. In this way, the bag-of-encrypted-words (BOEW) model is built to represent each image by a feature vector, i.e., a normalized histogram of the encrypted visual words. The similarity between images can be directly measured by the Manhattan distance between feature vectors on the cloud server side. Experimental results and security analysis on the proposed scheme demonstrate its search accuracy and security.

Research on the Identification of Hand-Painted and Machine-Printed Thangka Using CBIR

Thangka is a unique painting art form in Tibetan culture. As Thangka was awarded as the first batch of national intangible cultural heritage, it has been brought into focus. Unfortunately, illegal merchants sell fake Thangkas at high prices for profit. Therefore, identifying hand-painted Thangkas from machine-printed fake Thangkas is important for protecting national intangible cultural heritage. The paper uses Content-Based Image Retrieval (CBIR) techniques to analyze the color, shape, texture, and other characteristics of hand-painted and machine- printed Thangka images, in order to identify Thangkas. Based on the database collected and established by this project team, we use Local Binary Pattern (LBP) texture analysis combined with the color histogram of Hue,aturation, Value (HSV) space, scale invariance, K-Means clustering, perceptual Difference Hash (DHASH) and other algorithms to extract the color lines and texture features of Thangka images, in order to identify hand-painted and machine-printed Thangkas. Three algorithms, LBP algorithm and HSV algorithm and DHASH algorithm, are compared, and the experimental results show that the color histogram algorithm based on HSV space is efficient. This algorithm can be applied broadly to retrieve and identify hand-painted Thangkas and help protect this precious intangible cultural heritage.

Content-Based Image Retrieval Using Angles Across Scales

This letter proposes a content-based image retrieval technique using novel dense angle descriptor and dictionary learning (DL). The histogram of oriented gradients (HOG) descriptor fails to obtain rotation invariance and well-defined rotation behavior, and therefore, a dense angle-based HOG descriptor has been presented to address the image rotation invariance. The technique computes angles across multiple scales and uses bag-of-visual features at different scales for DL. Experiments conducted on building and remote sensing datasets show that the proposed technique achieves high retrieval performance.

Query-Based Image Retrieval using SVM

Now a day’s image plays an important role to extract the information about the object in various industries. Many traditional methods have been employed to retrieve images. It interactively determines the user’s query by asking the user whether the image is relevant or not. In this information tech age, graphics have become a major portion of information processing. In the Image registration processing, the image plays an important part to extract the information regarding the item in a variety of fields including in tourism, medical and geological, weather systems calling. There are lots of Approaches individuals who are used to recover images. It interactively determines an individual's query by requesting the users whether the image will be relevant (similar) or maybe not. In content-based image retrieval (CBIR) system, effective company of this image database used to improve the functioning of the procedure. The research of content-based image retrieval (CBIR) technique has become a significant research topic. Being an individual, we've Studied and done investigation of various features in this manner or in mixes. We found that image Registration processing (IRP) is the key area in above mentioned industries. Various research papers through color feature and texture feature extraction were studied and concluded that point cloud data structure is best for image registration process using Iterative Closest Point (ICP) algorithm.

State of the art content based image retrieval techniques using deep learning: a survey

State of the art content based image retrieval techniques using deep learning: a survey

A ten-year literature review of content-based image retrieval (CBIR) studies in the tourism industry

Purpose Due to the worldwide growth of digital image sharing and the maturity of the tourism industry, the vast and growing collections of digital images have become a challenge for those who use and/or manage these image data across tourism settings. To overcome the image indexing task with less labour cost and improve the image retrieval task with less human errors, the content-based image retrieval (CBIR) technique has been investigated for the tourism domain particularly. This paper aims to review the relevant literature in the field to understand these previous works and identify research gaps for future directions. Design/methodology/approach A systematic and comprehensive review of CBIR studies in tourism from the year 2010 to 2019, focussing on journal articles and conference proceedings in reputable online databases, is conducted by taking a comparative approach to critically analyse and address the trends of each fundamental element in these research experiments. Findings Based on the review of the literature, the trends of CBIR studies in tourism is to improve image representation and retrieval by advancing existing feature extraction techniques, contributing novel techniques in the feature extraction process through fine-tuning fusion features and improving image query of CBIR systems. Co-authorship, tourist attraction sector and fusion image features have been in focus. Nonetheless, the number of studies in other tourism sectors and available image databases could be further explored. Originality/value The fact that no existing academic review of CBIR studies in tourism makes this paper a novel contribution.

Unsupervised defect detection algorithm for printed fabrics using content-based image retrieval techniques

Automatic detection of printing defect technology is significant for improving printing fabrics’ appearance and quality. In this research, we proposed an unsupervised printing defect detection method by processing the difference map between the test image and the reference image. Aimed at this, we adopted a content-based image retrieval (CBIR) method to retrieve the reference image, which includes an image database, a convolutional denoising auto-encoder (CDAE) and a hash encoder (HE): the elements of image database are extracted from only one defect-free sample image of the test fabric; the CDAE prevents the system being affected by the texture of the fabric and provides a reliable feature description of the patterns; the HE indexes the feature vectors to binary code while maintaining their similarity; both CDAE and HE are trained in an unsupervised manner. With the retrieved reference image, the defect is determined by applying the Tsallis entropy thresholding and opening operation on the difference map. The method can be implemented without labeled and defective samples, and without consideration of the periodical primitive of patterns. Experimental results demonstrate the effectiveness and efficiency of the proposed method in defect detection for printed fabrics with complex patterns.

An analysis of content-based image retrieval

Nowadays, working on digital images is gaining much popularity in multimedia systems, due to the rapid increase in the utilization of large image databases. Thus, the Content-Based Image Retrieval (CBIR) method has become the most valuable method for these databases. This study mainly focuses on content-based image retrieval; which uses image features like color, shape, texture, etc. by searching the user query image from a large image database based on user request. CBIR is the most widely used technique as its searching capability is faster than the other traditional methods, and it works well in retrieving images automatically. It is also a big alternative approach to traditional methods. The CBIR techniques are used in many applications like surveillance detection, crime avoidance, fingerprint identification, E-library, medical, historical monument and biodiversity information systems, and many more. A total of 38 CBIR articles were comparatively analyzed.

An Efficient Two-layer based Technique for Content-based Image Retrieval

The rapid advancement and exponential evolution in the multimedia applications raised the attentional research on content-based image retrieval (CBIR). The technique has a significant role for searching and finding similar images to the query image through extracting the visual features. In this paper, an approach of two layers of search has been developed which is known as two-layer based CBIR. The first layer is concerned with comparing the query image to all images in the dataset depending on extracting the local feature using bag of features (BoF) mechanism which leads to retrieve certain most similar images to the query image. In other words, first step aims to eliminate the most dissimilar images to the query image to reduce the range of search in the dataset of images. In the second layer, the query image is compared to the images obtained in the first layer based on extracting the (texture and color)-based features. The Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) were used as texture features. However, for the color features, three different color spaces were used, namely RGB, HSV, and YCbCr. The color spaces are utilized by calculating the mean and entropy for each channel separately. Corel-1K was used for evaluating the proposed approach. The experimental results prove the superior performance of the proposed concept of two-layer over the current state-of-the-art techniques in terms of precision rate in which achieved 82.15% and 77.27% for the top-10 and top-20, respectively.

Visitor-artwork network analysis using object detection with image-retrieval technique

Recent museum exhibitions are becoming a means by which to satisfy visitor demands. In order to provide visitor-centric exhibitions, artwork must be analyzed based on the behavior of visitors, and not merely according to museum professionals' points of view. This study aims to analyze the relationship between museum visitors and artwork via a network analysis based on visitor behavior using object detection techniques. Cameras installed in a museum recorded visitors, and an object detector with a content-based image-retrieval technique tracked visitors from the videos. The durations spent with different artworks were measured, and the data was converted into a bipartite graph. The relationships between different artwork types were analyzed with a visitor-centered artwork network. Based on the visitors’ behavior, significant artworks were identified and the artwork network was compared to the arrangement of the museum. The tendency of edges in the artwork network was also examined considering visitors' preferences for artworks. The method used here makes it possible to collect quantitative data, with the results possibly used as a basis and for reference when analyzing artwork in a visitor-centered approach.

Brain Tumour Categorization with MRI Representation by Feature Extraction using GLCM and Support Vector Machine

Cluster of tissue that influence the normal tissue by moderate expansion of irregular cells is known as Brain tumour. It happens when cell get anomalous development inside the brain and this remains the primary reason in increasing the fatality rate among humans. Among a wide range of cancers, brain tumour is incredibly serious and to avoid life threats of an individual, quick diagnosing and treatment must be given. Identifying these cells is a problematic issue, due to formation of tumour cells. It is exceptionally crucial to classify brain tumour from MRI image. In our proposed work, MRI images retrieved by utilizing Content based image retrieval technique are taken as input for classification. To achieve accuracy in classification and for efficient segmentation, pre-processing is carried for color conversion, noise reduction and resizing. Segmentation of tumour cells is done by Expectation and Maximization technique to know about region of affected cells present in that segmented area. This is trailed by statistical and size-based feature extraction by Gray Level Co-Occurrence Matrix from segmented images. The features extracted from segmented portion will be trained to analyze the presence of tumour in given MR images. The brain tumor classification, done in MATLAB environment allows localizing a mass of abnormal cells in a slice of MRI image using SVM Classifier, which is quickest method and furthermore give the great accuracy in classification. Experimental results accomplished accuracy of 100% in distinguishing the tissues as normal or abnormal from MR images exhibiting the viability of proposed method.

Brain Tumour Categorization with MRI Representation by Feature Extraction using GLCM and Support Vector Machine

Cluster of tissue that influence the normal tissue by moderate expansion of irregular cells is known as Brain tumour. It happens when cell get anomalous development inside the brain and this remains the primary reason in increasing the fatality rate among humans. Among a wide range of cancers, brain tumour is incredibly serious and to avoid life threats of an individual, quick diagnosing and treatment must be given. Identifying these cells is a problematic issue, due to formation of tumour cells. It is exceptionally crucial to classify brain tumour from MRI image. In our proposed work, MRI images retrieved by utilizing Content based image retrieval technique are taken as input for classification. To achieve accuracy in classification and for efficient segmentation, pre-processing is carried for color conversion, noise reduction and resizing. Segmentation of tumour cells is done by Expectation and Maximization technique to know about region of affected cells present in that segmented area. This is trailed by statistical and size-based feature extraction by Gray Level Co-Occurrence Matrix from segmented images. The features extracted from segmented portion will be trained to analyze the presence of tumour in given MR images. The brain tumor classification, done in MATLAB environment allows localizing a mass of abnormal cells in a slice of MRI image using SVM Classifier, which is quickest method and furthermore give the great accuracy in classification. Experimental results accomplished accuracy of 100% in distinguishing the tissues as normal or abnormal from MR images exhibiting the viability of proposed method.

Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques.

In this research, A Deep Convolutional Neural Network was proposed to detect Pneumonia infection in the lung using Chest X-ray images. The proposed Deep CNN models were trained with a Pneumonia Chest X-ray Dataset containing 12,000 images of infected and not infected chest X-ray images. The dataset was preprocessed and developed from the Chest X-ray8 dataset. The Content-based image retrieval technique was used to annotate the images in the dataset using Metadata and further contents. The data augmentation techniques were used to increase the number of images in each of class. The basic manipulation techniques and Deep Convolutional Generative Adversarial Network (DCGAN) were used to create the augmented images. The VGG19 network was used to develop the proposed Deep CNN model. The classification accuracy of the proposed Deep CNN model was 99.34 percent in the unseen chest X-ray images. The performance of the proposed deep CNN was compared with state-of-the-art transfer learning techniques such as AlexNet, VGG16Net and InceptionNet. The comparison results show that the classification performance of the proposed Deep CNN model was greater than the other techniques.

Impact of Multi-Feature Extraction on Image Retrieval and classification Using Machine Learning Technique

Vast information generated due to the web requires proper mechanisms and tools for efficient management and retrieval of images. This led to the development of an efficient image retrieval system. Image retrieval is the process of retrieving relevant images from a large database. Text-Based Image Retrieval (TBIR) and Content-Based Image Retrieval (CBIR) are the two efficient techniques for image retrieval. It has become an active area of research for image retrieval. Most of the systems are designed with three basic phases in CBIR, i.e. feature extraction, feature selection and similarity matching. Feature extraction can be done based on the colour, shape or texture of the image. The studies conducted so far involve CBIR systems which utilize individual features or only two or more features combined for feature extraction by applying different feature extraction and CBIR techniques. The proposed system illustrates a comparison between the performance of the existing CBIR algorithm with multi-feature extraction using discrete wavelet transform, edge histogram descriptor, sobel operator, moment invariant, histogram-oriented gradient, local binary pattern and classification using Support Vector Machine (SVM) based on the accuracy measures—precision and recall. The experimental results show that multi-feature extraction techniques give better results in some of the individual categories but retrieval results using machine learning is better with respect to average accuracy.

An Efficient Image Search for Content Based Image Retrieval using Semantic-Assisted Visual Hashing

The CBIR system can be used to recover medical images associated with different diseases. The purpose of medicine information system is about providing the right information, at the right time, in the right place, to the right person. This improves the quality and efficiency of the patient care process. In clinical decision manufacturing process, it is important to find other images for a given query. . This is done using the content-based image recovery technique. Data mining is the technique of sorting through massive figured devices in get awareness about the human habits and place up human relationships to resolve problems via figure evaluation. Data gold mining apparatus gives business to anticipate the future qualities. Quality content material-based search offers a large volume of picture selections totally, while both effectiveness and performance are essential problems. An advanced indexing form is essential to scale the large statistics, facilitate the correct assessment.. This is a new technology that supports scalable content-based image retrieval (CBIR) hashing. Recently, CBIR has been focused with the future directions in the field of research. The Unsupervised visual hashing approach called semantic assisted visual hashing (SAVH) has a semi-supervised and supervised visual blending process. This approach is based on a strictly hidden rich semantics integrated into auxiliary image texts to increase the efficiency of visual clutter without any semantic labels. The most native technique for CBIR is to examine query photographs with each sample saved in the database sequentially. Its linear intricacy results in the poor expressions and performance scalability in the actual environment. Also, visual features normally have high dimensions. Picture access is normally transported out while using the complementing features of any predicament picture with the types within the photo data source. It may end up being categorized as textual content, which is primarily contentbased. To extend the scope, an unsupervised framework is deviced to learn hash codes simultaneously preserving the visual similarities of the images, integrating the semantic assistance of the texts on modeling high inter-image relationships, and defining correlations between images and shared content. The present study recommends a book unsupervised visible hashing system, termed as SAVH, to perform visual hashing learning effectively.

Content-Based Medical Image Retrieval Using Delaunay Triangulation Segmentation Technique

This article presents a novel technique for retrieval of lung images from the collection of medical CT images. The proposed content-based medical image retrieval (CBMIR) technique uses an automated image segmentation technique called Delaunay triangulation (DT) in order to segment lung organ (region of interest) from the original medical image. The proposed method extracts novel and discriminant features from the segmented lung region instead of extracting novel features from the whole original image. For the extraction of shape features, the authors employ edge histogram descriptor (EHD) and geometric moments (GM), and for the extraction of texture features, the authors use gray-level co-occurrence matrix (GLCM) technique. The shape and texture features are combined to form the hybrid feature which is used for retrieval of similar lung images. The proposed method is evaluated using two benchmark datasets of lung CT images. The simulation results prove that the proposed CBMIR framework shows improved performance in terms of retrieval accuracy and retrieval time.

Content-based image retrieval system for HRCT lung images: assisting radiologists in self-learning and diagnosis of Interstitial Lung Diseases

Content-based Image Retrieval (CBIR) is a technique that can exploit the wealth of the data stored in a repository and help radiologists in decision making by providing references to the image in hand. A CBIR system for High-Resolution Computed Tomography (HRCT) lung images depicting signs of Interstitial Lung Diseases (ILDs) can be built and used as a self-learning tool for budding radiologists. The study of a few lung image retrieval systems available in the literature identifies some important issues that need to be taken care of. In most of the works, the creation of the reference database involves painstaking manual activity, which is time-consuming and needs skilled labor. A lot of human interventions are required, particularly for the proper delineation of the region of interest (ROI) that represents pathology in each of the images in a database. In most cases, the size of the ROIs representing different disease findings are fixed (i.e., either a fixed size square or circle), which at times may not be a proper representation of the disease pattern and as a consequence, it might limit the system’s performance. Until date, a few learning-based approaches have been developed for content-based image retrieval of HRCT lung images, which either learn the similarity using a classifier or get trained through relevance feedback. For medical image analysis, the availability of labelled data for learning makes these learning-based retrieval systems meaningful as it enhances their performance in contrast to their simple distance-based counterpart. The objective of this paper is to develop a CBIR system for ILDs that is reliable and needs minimal human intervention. The paper evaluates the performance of three popular segmentation algorithms. It identifies the best for the effective and automated delineation of an arbitrary region of interest (AROI) depicting the sign of ILDs on HRCT images of the thorax in contrast to the manual delineation of fixed size ROI. This minimizes the manual effort for the creation and maintenance of the reference database, as well as the manual delineation of AROI during query formation. Moreover, AROI created through the automated clustering is found to have a better representation of disease patterns. Three recently proposed general-purpose learning based CBIR techniques are implemented and tested for retrieval of HRCT lung images depicting the sign of ILDs. The best method is suggested after careful evaluation of all the competing techniques.