Content-based Image Retrieval Research Articles

A Hybrid Filtering Technique of Digital Images in Multimedia Data Warehouses

The similarity search approach used for image Data Warehouse (DW) can provide better insights into discovering the most similar images compared to the input query. Due to the later innovation improvement, the mixed media complexity is discernibly expanded and modern inquires about regions are opened depending on comparable mixed media substance recovery. Content-Based Image Retrieval (CBIR) algorithms are utilized for the retrieval of images related to the inquiry image from gigantic databases or DW. The queries that are used for DW are complex, take a lot of time to process and many give less accurate results. For these reasons, this paper needs to have an effective technique to improve the similarity search query process that reflects a more positive result. In this paper, show how to extract features from a set of images (color, shape, and texture features) by using CBIR algorithm with Color Edge Detection (CED) method. Once these features are extracted, the proposed method will minimize the distance between these features vectors and the query image one using a Genetic Algorithm (GA). This paper illustrates the extraction of endless strong and imperative features from the database of the images, therefore, the capacity of these features in storing within the frame of features vectors. Accordingly, an imaginative closeness assessment with a metaheuristic algorithm (Genetic Algorithm (GA) with Simulating Annealing (SA)) has been attained between the query image features and those having a place in the database image. This paper introduces a new algorithm CEDF (Color Edge Detection with Gaussian Blur Filter) that applies the Gaussian Blur Filter after using CED method for feature detection of the image. Experimental results show that CEDF method gives better result than the other already-known methods.

Fuzzy adaptive learning control network (FALCN) for image clustering and content-based image retrieval on noisy dataset

<abstract> <p>It has been demonstrated that fuzzy systems are beneficial for classification and regression. However, they have been mainly utilized in controlled settings. An image clustering technique essential for content-based picture retrieval in big image datasets is developed using the contents of color, texture and shape. Currently, it is challenging to label a huge number of photos. The issue of unlabeled data has been addressed. Unsupervised learning is used. K-means is the most often used unsupervised learning algorithm. In comparison to fuzzy c-means clustering, K-means clustering has lower-dimensional space resilience and initialization resistance. The dominating triple HSV space was shown to be a perceptual color space made of three modules, S (saturation), H (hue) and V (value), referring to color qualities that are significantly connected to how human eyes perceive colors. A deep learning technique for segmentation (RBNN) is built on the Gaussian function, fuzzy adaptive learning control network (FALCN), clustering and the radial basis neural network. The segmented image and critical information are fed into a radial basis neural network classifier. The suggested fuzzy adaptive learning control network (FALCN) fuzzy system, also known as the unsupervised fuzzy neural network, is very good at clustering images and can extract image properties. When a conventional fuzzy network system receives a noisy input, the number of output neurons grows needlessly. Finally, random convolutional weights extract features from data without labels. Furthermore, the state-of-the-art uniting the proposed FALCN with the RBNN classifier, the proposed descriptor also achieves comparable performance, such as improved accuracy is 96.547 and reduced mean squared error of 36.028 values for the JAFE, ORL, and UMIT datasets.</p> </abstract>

Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters

Optimised Image Storage and Retrieval on Hadoop

With the exponential growth of data, it is difficult to efficiently store and retrieve data using traditional methods. There is a need to optimize the storage and to efficiently retrieve relevant data matching the user query. Traditional methods lack optimized storage and to effectively retrieve data. To overcome these limitations, in this project, we propose a distributed architecture framework to optimize memory usage and to effectively retrieve relevant data using Content-Based Image Retrieval (CBIR). The experimental results show that the proposed model enhances storage performance and retrieval time by 20%.

A Visual Target Representation using Saliency Detection Approach

The task of saliency detection is to identify the most important and informative part of a scene. Saliency detection is broadly applied to numerous vision problems, including image segmentation, object recognition, image compression, content-based image retrieval, and moving object detection. Existing saliency detection methods suffer a low accuracy rate because of missing components of saliency regions. This study proposes a visual saliency detection method for the target representation to represent targets more accurately. The proposed method consists of five modules. In the first module, the salient region is extracted through manifold ranking on a graph, which incorporates local grouping cues and boundary priors. Secondly, using a region of interest (ROI) algorithm and the subtraction of the salient region from the original image, other parts of the image, either related or nonrelated to the interested target, are segmented. Lastly, those related and non-related regions are classified and distinguished using our proposed algorithm. Experimental result shows that proposed salient region accurately represent the interested target which can be used for object detection and tracking applications.

Supervised Contrastive Learning Based on Fusion of Global and Local Features for Remote Sensing Image Retrieval

With the rapid development of remote sensing sensor technology, the number of remote sensing images (RSIs) has exploded. How to effectively retrieve and manage these massive data has become an urgent problem. At present, content-based image retrieval (CBIR) methods have become a mainstream method due to their excellent performance. However, most of the existing retrieval methods only consider the global features of images, which lacks the ability to discriminate images with the same semantic information but different visual representations. To alleviate this issue, supervised contrastive learning based on the fusion of global and local features method is proposed in this paper, named SCFR. Firstly, a fusion module is designed to combine global and local features to enhance the ability of image expression. Secondly, supervised contrastive learning is introduced into the retrieval task to effectively improve the feature distribution, so that the positive sample pairs are close to each other, and the negative sample pairs are far away from each other in the feature space. Furthermore, to make the distribution of features of the same class more compact, the center contrastive loss is added to the constraints, and combines the class centers that change iteratively with the network. Experimental results on three RSI datasets show that our proposed method has more effective retrieval performance than the state-of-the-art methods. The code and models are available at https://github.com/xdplay17/SCFR.

Multimodal Fused Deep Learning Networks for Domain Specific Image Similarity Search

The exponential increase in data over the past few years, particularly in images, has led to more complex content since visual representation became the new norm. E-commerce and similar platforms maintain large image catalogues of their products. In image databases, searching and retrieving similar images is still a challenge, even though several image retrieval techniques have been proposed over the decade. Most of these techniques work well when querying general image databases. However, they often fail in domain-specific image databases, especially for datasets with low intraclass variance. This paper proposes a domain-specific image similarity search engine based on a fused deep learning network. The network is comprised of an improved object localization module, a classification module to narrow down search options and finally a feature extraction and similarity calculation module. The network features both an offline stage for indexing the dataset and an online stage for querying. The dataset used to evaluate the performance of the proposed network is a custom domain-specific dataset related to cosmetics packaging gathered from various online platforms. The proposed method addresses the intraclass variance problem with more precise object localization and the introduction of top result reranking based on object contours. Finally, quantitative and qualitative experiment results are presented, showing improved image similarity search performance.

Image-based searches for pulsar candidates using MWA VCS data

Abstract Pulsars have been studied extensively over the last few decades and have proven instrumental in exploring a wide variety of physics. Discovering more pulsars emitting at low radio frequencies is crucial to further our understanding of spectral properties and emission mechanisms. The Murchison Widefield Array Voltage Capture System (MWA VCS) has been routinely used to study pulsars at low frequencies and discover new pulsars. The MWA VCS offers the unique opportunity of recording complex voltages from all individual antennas (tiles), which can be off-line beamformed or correlated/imaged at millisecond time resolution. Devising imaged-based methods for finding pulsar candidates, which can be verified in beamformed data, can accelerate the complete process and lead to more pulsar detections. Image-based searches for pulsar candidates can reduce the number of tied-array beams required, increasing compute resource efficiency. Despite a factor of $\sim$ 4 loss in sensitivity, searching for pulsar candidates in images from the MWA VCS, we can explore a larger parameter space, potentially leading to discoveries of pulsars missed by high-frequency surveys such as steep spectrum pulsars, exotic binary systems, or pulsars obscured in high-time resolution time series data by propagation effects. Image-based searches are also essential to probing parts of parameter space inaccessible to traditional beamformed searches with the MWA (e.g. at high dispersion measures). In this paper we describe the innovative approach and capability of dual-processing MWA VCS data, that is forming 1-s visibilities and sky images, finding pulsar candidates in these images, and verifying by forming tied-array beam. We developed and tested image-based methods of finding pulsar candidates, which are based on pulsar properties such as steep spectral index, polarisation and variability. The efficiency of these methodologies has been verified on known pulsars, and the main limitations explained in terms of sensitivity and low-frequency spectral turnover of some pulsars. No candidates were confirmed to be a new pulsar, but this new capability will now be applied to a larger subset of observations to accelerate pulsar discoveries with the MWA and potentially speed up future searches with the SKA-Low.

A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images

A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images

A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images

A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images

Advanced Evaluation Method for Video Retrieval System

n the past few years, the multimedia storage has increased and costs of storing multi-media data has become cheaper. Which is why, there are large numbersof videos that are available in the video repositories. With development of the multi-media data types and available bandwidths.The proposed method transferseach video of database into scenes using color histogrambased scene change detection algorithm and key framesare extracted. For key frames multiple features areobtained using straight forward rules. A new framework depending on CNNs (convolutional neural networks) is recommended to perform the content based video retrieval with the less storage cost also with higher search capability. The recommended framework subsists of extraction algorithm with respect to key-frame and the feature collection strategies. Particularly, the extraction algorithm of key-frame takes benefit of clustering idea; so in that case excessive information is taken out from video data and also the storage cost is highly shortened. This work present a method uses the extracted features with convolutional neural network (CNN) for classification tasks. In this research paper, different types of videos will be used and the important features will be extracted using SIFT, after that we use the deep learning(CNN) process will be performed. The experimental results showed the efficiency and effectiveness of suggested approach

A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations

A Novel Approach for Content-based Image Retrieval System using Logical AND and OR Operations

Stacked Siamese Neural Network (SSiNN) on Neural Codes for Content-Based Image Retrieval

Stacked Siamese Neural Network (SSiNN) on Neural Codes for Content-Based Image Retrieval

Enhanced Content-Based Image Retrieval with Trio-Deep Feature Extractors with Multi-Similarity Function

Enhanced Content-Based Image Retrieval with Trio-Deep Feature Extractors with Multi-Similarity Function

Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

In the field of digital data management, content-based image retrieval (CBIR) has become one of the most important research areas, and it is used in many fields. This system searches a database of images to retrieve most visually comparable photos to a query image. It is based on features derived directly from the image data, rather than on keywords or annotations. Currently, deep learning approaches have demonstrated a strong interest in picture recognition, particularly in extracting information about the features of the image. Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant images. The investigations are conducted using a publicly available dataset named Corel, and the f-measure, recall, and precision metrics are used for performance assessment. Investigation outcomes show that the proposed technique outperforms the existing image retrieval techniques.

Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as necessary-but-not-sufficient precondition of the downstream in a new notion of Space Economy 4.0 - Part 1: Problem background in Artificial General Intelligence (AGI)

ABSTRACT Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical sensory image-derived semantics-enriched Analysis Ready Data (ARD) product-pair and process gold standard as linchpin for success of a new notion of Space Economy 4.0. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, it is regarded as necessary-but-not-sufficient “horizontal” (enabling) precondition for: (I) Transforming existing EO big raster-based data cubes at the midstream segment, typically affected by the so-called data-rich information-poor syndrome, into a new generation of semantics-enabled EO big raster-based numerical data and vector-based categorical (symbolic, semi-symbolic or subsymbolic) information cube management systems, eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery. (II) Boosting the downstream segment in the development of an ever-increasing ensemble of “vertical” (deep and narrow, user-specific and domain-dependent) value–adding information products and services, suitable for a potentially huge worldwide market of institutional and private end-users of space technology. For the sake of readability, this paper consists of two parts. In the present Part 1, first, background notions in the remote sensing metascience domain are critically revised for harmonization across the multi-disciplinary domain of cognitive science. In short, keyword “information” is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation. Moreover, buzzword “artificial intelligence” is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI. Second, based on a better-defined and better-understood vocabulary of multidisciplinary terms, existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system. To overcome their drawbacks, an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.

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%.

Madhubani Art Classification using transfer learning with deep feature fusion and decision fusion based techniques

Some traditional Indian art forms enjoy widespread popularity across the world. One of the most prominent among these is the Madhubani style. This art form’s rich heritage and beauty enthrall the connoisseurs and continue to inspire new designs catering to the changing tastes of prevalent fashion. Preservation of these traditional art forms is the need of the hour. Modern technological advances can be utilized with great advantage for this purpose. Since a database of Madhubani art forms was hitherto unavailable, an attempt is made in this work to create one from scratch. Five different classes of Madhubani art, i.e., Bharni, Godna, Kachni, Kohbar, and Tantrik, are identified, and the collected images are annotated with these classes. Classification of the art images is attempted using the handcrafted Local Binary Pattern (LBP) texture descriptors and state-of-the-art Convolutional Neural Networks (CNNs). The Transfer Learning approach with CNNs is employed to classify the designs. An attempt is made to obtain a better classification accuracy than the one provided by standard CNNs. Towards this end, the current work proposes a fusion of features extracted from several deep CNNs, decision fusion-based classification based on averaging prediction score (FAVG), and maximum vote score (FMAX). The proposed method’s performance is tested on our Madhubani art dataset and compared against several standard pre-trained CNNs available in the literature. The proposed approaches provide significantly higher classification accuracy for Madhubani art patterns, with decision fusion based on averaging prediction score (FAVG) approach being the best. The maximum accuracy, specificity, and error rate scores are 98.82%, 99.72%, and 1.18%, respectively. This is the first such attempt, and the excellent results motivate further work to develop content-based image retrieval tools and evolutionary design-based tools for automating the development of new designs. These endeavors are expected to go a long way in preserving precious art heritage and fostering its rapid growth in the world market. The dataset will be made publically available for further experimentation.

Tea-cake CBIR: a tea-cake content-based image retrieval model with rich and intensive feature extraction

Tea-cake CBIR: a tea-cake content-based image retrieval model with rich and intensive feature extraction

BPPIR: Blockchain-assisted privacy-preserving similarity image retrieval over multiple clouds

With the progress of the times and the development of electronic products, more and more images are captured and stored on multiple cloud servers, which are more likely to exhibit dishonesty because of interdependence rather than semi-honesty and thus how to achieve search over multiple clouds becomes intractable. Moreover, most of the existing research works on content-based image retrieval so far do not consider dishonest clouds and thereby do not support multi-cloud retrieval. On the one hand, a central search server is generally introduced for the convenience of retrieval in the system with multiple cloud servers. However, the central search server may return false or incomplete search results for possible commercial profit, which reduces the retrieval accuracy. On the other hand, cloud servers may destroy the image integrity by deleting or tampering images to save computing and bandwidth resources, resulting in the loss of important information for the image owner and incorrect decryption of the image for the user. In order to address these two problems, we propose a blockchain-assisted privacy-protected similarity image retrieval in the multiple cloud environment, which can not only ensure the accuracy of the retrieval results, but also can verify the integrity of images. The performance of this scheme is evaluated on a real-world dataset, and the results show the high retrieval precision and efficiency of this scheme.