Content-based Image Retrieval Process Research Articles

A DCT probability histogram-based ROI features for content-based natural and medical image retrieval applications

The effectiveness of a Content-based Image Retrieval (CBIR) system depends on selecting representative and salient visual features. Capturing the salient image features for forming a feature vector is an essential part of the CBIR process, as it can significantly affect retrieval efficiency. Most images contain a visually prominent region that perceives maximum image semantic information, known as Regions-of-Interest (ROIs). This paper suggests a new CBIR scheme that employs a Graph-based Visual Saliency (GBVS) map to extract the ROI of the image. Further, block-level Discrete Cosine Transforms (DCT) and subsequent histograms are computed for the DC and significant AC coefficients of the extracted ROI image. In addition, a low-dimensional feature vector is formed from the modified histograms. Subsequently, six statistical features from the background scene have also been computed for the final feature vector construction. As a result, the final feature vector contains ROI and background information in their proper proportion to improve retrieval performance. Moreover, the performance of the proposed CBIR scheme has been evaluated using one medical, three natural, two objects, one texture, and one natural heterogeneous image database. All these databases collectively contain 53732 different images from 200 image classes. The outcomes of the experiments demonstrate substantial improvement in the retrieval process with some existing related schemes. At the same time, it also shows that the performance of the proposed scheme is robust concerning all kinds of image databases.

An optimal approach for content-based image retrieval using deep learning on COVID-19 and pneumonia X-ray Images

Analyzing and comparing the multi-spectral images in the healthcare domain is a complex task as it involves features in the visible spectrum and beyond. Using Content-based image retrieval (CBIR) with deep learning in the healthcare domain provides an ease to identify similar images, which eventually helps in analysis. CBIR, with deep learning, removes the explicit definition of features and autonomously determines similarity on multiple attributes like shape, color, and texture. The proposed method uses multiple deep learning architectures fine-tuned by using max-pool overlapping pooling in all networks and placing the same number of decision layers with the same parameters to determine the result based on feature extraction layers. State-of-the-art Neural Network models VGG-16, VGG-19, Xception, InceptionResNetV2, DenseNet201, MobileNetV2, and NASNetLarge were considered for the experiment to identify the most optimal model. First, a classification process is applied to compare the performance of the state-of-the-art networks. Then, the CBIR process is applied using the feature extraction part. The experiment was conducted on the chest X-rays dataset, consisting of 21,165 images with COVID-19, pneumonia, and normal, with and without rotational invariant cases. The VGG-16 model proved to be the most optimal choice for image retrieval and achieved the highest precision of 99% and mAP of 94.34% compared to recent CBIR methods, which also used chest X-rays datasets. Rotational invariant cases were also tested and achieved mean precision of 86%.

Retrieving Encrypted Images Using Convolution Neural Network and Fully Homomorphic Encryption

A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.94% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.

Improved Content Based Image Retrieval Process Based on Deep Convolutional Neural Network and Salp Swarm Algorithm

Digital image and medical image retrieval from several repositories are improving gradually, so the capacity of repositories increases rapidly. The semantic space is the main issue on content-based image retrieval (CBIR), which exists among the semantic level as well as increases the data recognized through human and low level visible data obtained through the image. The CBIR system utilizes the deep convolutional neural network (DCNN), which is trained to medical image characterization and the digital image by salp swarm optimization algorithm (SSA). The average classification accuracy for medical image is 86.805%, a mean average precision is 79%, Average Recall Rate (ARR) is 91.7% and [Formula: see text]-measure is 84.9%, are achieved during retrieval task. For image retrieval, the Average Precision Rate (APR) improved from 39%, 40%, 36% and 42.5% to 86.8% and the ARR enhanced from 39.5%, 40.5%, 35.5% and 42.5% to 86.8%. The [Formula: see text]-measure is improved from 39.5%, 40.5%, 35.5% and 42.5% to 86.8% as different with Local tetra patterns (LTrP), LOOP, local derivative pattern (LDP) and local mean differential excitation pattern (LMDeP) separately on Corel-1K dataset. The presented method is most suitable for multimodal digital images and medical image retrieval for various parts of the body.

Image Feature Synthesis and Matching in Content-Based Image Retrieval System – A Review

One of the important concepts in information & data analytics is the content-based image retrieval process. We are living in the information age. In the modern-day digital information technology imaging world, this is playing a predominant role in different sectors ranging from defense to research fields. Content-based image retrieval, also known as query by image content is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images or textual matters in large databases. The usage of digital images has been increased enormously from the last decade due to the drastic growth in storage & network technology. These technological changes have led professional users to use, store and manipulate remotely stored images. Information Retrieval (IR) deals with the location and retrieval of related documents or images based on user inputs such as keywords or examples as a query from the repository. This has motivated us to take up the research work on the CBIR concepts. Hence, to throw light into this chosen research topic, we are carrying out extensive research on the image feature synthesis and matching in content-based image retrieval systems using the concepts of AI, ML & Fuzzy Logic schemes, which could be used to improve the retrieval system's performance in CBIRs. A brief survey, i.e., an insight into the chosen research area in the field of content-based image retrievals was made & the same is being presented w.r.t. the work done by various researchers across the globe in the form of an extensive literature review. The work done by them was studied, lacunas observed & the problem was defined with a couple of good objectives to be solved four objectives were proposed as O1 - Investigation of the effectiveness of evolutionary computation in generating composite operator vectors for image, so that feature dimensionality is reduced to improve retrieval performances; O2 - Construction of the image-leve

The Study of Approaches of Content Based Image Retrieval

In a content-based image retrieval system (CBIR), the most issue is to extract the image features that effectively represent the image contents as more information. Such an extraction needs a detailed analysis of retrieval performance of image options and in order to increase the retrieval efficiency of an image, there are various algorithms represented in a content based image retrieval process and such algorithms are explained here and this paper attempts to provide a very effective survey of the recent technical approaches in the current world of image retrieval. The recent publications are included in this literature survey covering all the aspects of the research in the content based image retrieval, including the images that has low image feature extractions and also tells the effective categorization of the image retrieval.

Weighted feature voting technique for content-based image retrieval

A content-based image retrieval process is used to retrieve most similar images to a query from a large database of images on the basis of extracted features. Matching measures are used to find similar images by measuring how the query features are close to the features of other images in the database. In this paper, a multi-features system is proposed which incorporates more than one feature in the retrieval process. The weights of these features are calculated based on the precision of each feature to reflect its importance in the retrieval process. These weights are used in a weighted feature voting technique to incorporate the role of each feature in extracting the relevant images. Also, different distance measures are used to get the highest precision of each feature. The results of applying the multi-features and multi-distances measures technique outperform other existing methods with accuracy 86.5% for Wang database, 86.5% for UW database and 85% for Caltech101 database.

Weighted Feature Voting Technique for Content Based Image Retrieval

A content-based image retrieval process is used to retrieve most similar images to a query from a large database of images on the basis of extracted features. Matching measures are used to find similar images by measuring how the query features are close to the features of other images in the database. In this paper, a multi-features system is proposed which incorporates more than one feature in the retrieval process. The weights of these features are calculated based on the precision of each feature to reflect its importance in the retrieval process. These weights are used in a weighted feature voting technique to incorporate the role of each feature in extracting the relevant images. Also, different distance measures are used to get the highest precision of each feature. The results of applying the multi-features and multi-distances measures technique outperform other existing methods with accuracy 86.5% for Wang database, 86.5% for UW database and 85% for Caltech101 database.

Co-occurrence of adjacent sparse local ternary patterns: A feature descriptor for texture and face image retrieval

Searching for similar images in large databases is a time-consuming work and an efficient image retrieval system is valuable in this situation. Texture is a prominent feature of image which can extract contents of image, hence a good texture feature extractor is necessary for content based image retrieval process. In this paper, a new texture descriptor is developed which is a combination of Local Ternary Pattern (LTP) and gray level co-occurrence matrix (GLCM). This feature descriptor which is named as CoALTP, inherits the attributes of both LTP and GLCM. First LTPs of pixels are obtained and then using GLCM in four directions, co-relations between pixel pairs are calculated as features. Two texture databases and a face images dataset are used for evaluation of proposed descriptor and results are compared with other local feature descriptors.

A semi-supervised probabilistic model for clustering large databases of complex images

Image content clustering is an effective way to organize large databases thereby making the content based image retrieval process much easier. However, clustering of images with varied background and foreground is quite challenging. In this paper, we propose a novel image content clustering paradigm suitable for clustering large and diverse image databases. In our approach images are represented in a continuous domain based on a probabilistic Gaussian Mixture Model (GMM) with the images modeled as mixture of Gaussian distributions in the selected feature space. The distance metric between the Gaussian distributions is defined in the sense of Kullback–Leibler (KL) divergence. The clustering is done using a semi-supervised learning framework where labeled data in the form of cluster templates is used to classify the unlabelled data. The clusters are formed around initially chosen seeds and are updated in the due course based on user inputs. In our clustering approach the user interaction is done in a structured way as to get maximum inputs from the user in a limited time. We propose two methods to carry out the structured user interaction using which the cluster templates are updated to improve the quality of the clusters formed. The proposed method is experimentally evaluated on benchmark datasets that are specifically chosen to include a wide variation of images around a common theme that is typically encountered in applications like photo-summarization and poses a major semantic gap challenge to conventional clustering approaches. The experimental results presented demonstrate the effectiveness of the proposed approach.

A new matching strategy for content based image retrieval system

Adopting effective model to access the desired images is essential nowadays with the presence of a huge amount of digital images. The present paper introduces an accurate and rapid model for content based image retrieval process depending on a new matching strategy. The proposed model is composed of four major phases namely: features extraction, dimensionality reduction, ANN classifier and matching strategy. As for the feature extraction phase, it extracts a color and texture features, respectively, called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). However, integrating multiple features can overcome the problems of single feature, but the system works slowly mainly because of the high dimensionality of the feature space. Therefore, the dimensionality reduction technique selects the effective features that jointly have the largest dependency on the target class and minimal redundancy among themselves. Consequently, these features reduce the calculation work and the computation time in the retrieval process. The artificial neural network (ANN) in our proposed model serves as a classifier so that the selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image. In addition, the proposed model presents an effective feature matching strategy that depends on the idea of the minimum area between two vectors to compute the similarity value between a query image and the images in the determined class. Finally, the results presented in this paper demonstrate that the proposed model provides accurate retrieval results and achieve improvement in performance with significantly less computation time compared with other models.

Spatial and Grayscale Metadata for Similarity Searches of Image Databases

This paper presents a content-based image retrieval process wherein the user identifies a feature of interest using a region quadtree decomposition of the image, spatial statistics and histograms of the grayscale values of the feature definition are calculated, and the result is compared to a database of these same calculations that have been performed on similar images. The sum of squared differences between the indices calculated for the quads that form the feature of interest and corresponding quads in the database yields a ranked list of matching image tiles. In an analysis of Landsat 7 imagery of North Georgia and an IKONOS panchromatic image of Kalamazoo, Michigan, we found that the retrieval success rate depends on the spatial and spectral characteristics of the feature of interest and the configuration of quads used to define the feature.