Content-based Image Retrieval Techniques Research Articles

Neuromorphic computing spiking neural network edge detection model for content based image retrieval

ABSTRACT In contemporary times, content-based image retrieval (CBIR) techniques have gained widespread acceptance as a means for end-users to discern and extract specific image content from vast repositories. However, it is noteworthy that a substantial majority of CBIR studies continue to rely on linear methodologies such as gradient-based and derivative-based edge detection techniques. This research explores the integration of bioinspired Spiking Neural Network (SNN) based edge detection within CBIR. We introduce an innovative, computationally efficient SNN-based approach designed explicitly for CBIR applications, outperforming existing SNN models by reducing computational overhead by 2.5 times. The proposed SNN-based edge detection approach is seamlessly incorporated into three distinct CBIR techniques, each employing conventional edge detection methodologies including Sobel, Canny, and image derivatives. Rigorous experimentation and evaluations are carried out utilizing the Corel-10k dataset and crop weed dataset, a widely recognized and frequently adopted benchmark dataset in the realm of image analysis. Importantly, our findings underscore the enhanced performance of CBIR methodologies integrating the proposed SNN-based edge detection approach, with an average increase in mean precision values exceeding 3%. This study conclusively demonstrated the utility of our proposed methodology in optimizing feature extraction, thereby establishing its pivotal role in advancing edge centric CBIR approaches.

Advanced Image Processing Techniques for Medical Image Retrieval Using Visual Features and Distance Measures

Recently, there has been a rapid increase in the volume of medical image repositories because of the maximized utilization of digitized image data in hospitals. As a result, there is a complexity in querying and handling such large databases, which has led to the development of a novel Content-Based Medical Image Retrieval (CBMIR) technique. In this work, the CBMIR technique is developed using three types of visual features (i.e., color, texture and shape) along with 12 distance measures, and is optimized with the Crossover-Gravitational Search Algorithm (CRO-GSA). For ease, we named this technique Content-Based Medical Image Retrieval using Visual features with CRO-GSA (CBMIRVC). The initial step of our CBMIRVC technique is to extract three types of visual features from the images. Then, each type of feature is employed with a suitable distance measure, which is used for the computation of image similarities between the medical images in the database and a provided query image. The optimization of our proposed CBMIRVC technique is done by the CRO-GSA algorithm. This algorithm optimizes the CBMIRVC technique by identifying the optimal combinations of visual features and similarity measures. Additionally, optimal weights are computed in accordance with the three types of features for the three similarities. Experimental validations were done using retrieved cancer-related and endoscopic color images from databases holding numerous image categories. The experimental outcome shows that our proposed CBMIRVC technique outperforms other conventional image retrieval techniques by effectively retrieving similar images from large databases.

Content-based image retrieval based on corel dataset using deep learning

A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.

Refining Content-Based Segmentation for Prediction of Coffee Bean Quality

Coffee has substantial economic value and is a key foreign exchange source for numerous nations, including Indonesia. Moreover, it is a primary livelihood for many of the country's farmers. Recently, there have been challenges in accurately predicting the quality of coffee beans, primarily due to time, inconsistency, and imprecision issues. Consequently, this study delves into the application of region-growing segmentation and content-based image retrieval (CBIR) techniques to enhance the prediction of coffee bean quality. The proposed hybrid approach, which combines region growing and CBIR methods, aims to improve the precision for forecasting cacao bean quality. Additionally, the research introduces an automated tool that employs these hybrid techniques for quality prediction. The study conducted experiments using a dataset of 400 premium and 400 low-quality coffee beans sourced from the University of Syiah Kuala in Indonesia. The results of the experiments demonstrate a commendable precision rate of 85.4%, showcasing significant improvement compared to certain previous studies.

CONTENT BASED IMAGE RETRIVAL USING DIFFERENT CLUSTERING TECHNIQUES

CBIR (Content based image retrieval) is the software system for retrieving the images from the database by using their features. In CBIR technique, the images are retrieved from the dataset by using the features like color, text, shape,texture and similarity. Object recognition technique is used in CBIR. Research on multimedia systems and content-based image retrieval is given tremendous importance during the last decade. The reason behind this is the fact that multimedia databases handle text, audio, video and image information, which are of prime interest in web and other high end user applications. Content-based Image retrieval deals with the extraction of knowledge, image data relationship, or other patternsnot expressly keep within the pictures. It uses ways from computer vision, image processing, image retrieval, data retrieval, machine learning, database and artificial intelligence. Rule retrieval has been applied to large image databases. The proposedsystem gives average accuracy of 90%. Keywords— CBIR, Color feature, Shape feature, Texture feature, Feature extraction, Clustering, Image Retrieval.

A Diagnostic Study of Content-Based Image Retrieval Technique for Studying the CT Images of Lung Nodules and Prediction of Lung Cancer as a Biometric Tool

Content Based Medical Image Retrieval (CBMIR) can be defined as a digital image search using the contents of the images. CBMIR plays a very important part in medical applications such as retrieving CT images and more accurately diagnosing aberrant lung tissues in CT images. The Content-Based Medical Image Retrieval (CBMIR) method might aid radiotherapists in examining a patient's CT image in order to retrieve comparable pulmonary nodes more precisely by utilizing query nodes. Intending a particular query node, the CBMIR system searches a large chest CT image database for comparable nodes. The prime aim of this research is to evaluate an end-to-end method for developing a CBIR system for lung cancer diagnosis.

Classification of medical X-ray images using supervised and unsupervised learning approaches

Most of the traditional approaches for medical image storage are least capable and scanning of relevant matching images are quite difficult. The existing approaches of content-based image retrieval (C-BIR) are less focused with medical images. The available research works with fuzzy logic approaches are very less and not efficient for medical image retrieval. Thus, there is a need of research work that can address both supervised and unsupervised learning approaches for medical image retrieval. Hence, the C-BIR technique is evolved with overcoming above stated concerns. Hence, this manuscript introduces two different C-BIR techniques using a support vector machine (SVM) and a fuzzy logic-based approach for classification. These approaches work on the classification based on feature extraction, region of Interest (ROI), corner detection, and similarity matching. The proposed approach has been analyzed for image retrieval for accuracy. The outcomes of the proposed study enhance the classification performances with retrieval than existing techniques of C-BIR.

An Improved and Efficient Content-Based Medical Image Retrieval Technique vs State-of-Art Texture Feature Extraction Techniques

Areas in the health sector such as X-rays, Dermatology, High-resolution Computed Tomography – HRCT, Endoscopy, Radiology, Cardiology and Magnetic Resonance Imaging – MRI heavily depend on medical images for their activities hence it has become complex for managing and accessing these images from their repositories. As a matter of concern content-based medical image retrieval-CBMIR has been the system proposed by many researchers for handling access to similar medical image(s) as the input image, yet there is a caveat that has to be addressed with respect to which technique best suits CBMIR system with respect to a given performance metric. Medical images are mostly of grayscale and therefore color feature extraction techniques may not work effectively on them. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric, this work seeks to comparatively evaluate the performance of the following state-of-the-art texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and a Proposed Technique using the metrics; Precision, Recall, F1-score, Mean Squared Error (MSE), Accuracy and Time. The results showed that the proposed technique is best suited for systems focusing on precision with an average precision score of 100% over 10.5k of raw medical images (dataset) using an appreciable minimum time with time complexity of 0(n).

EDBC Algorithm used for Content-Based Image Retrieval

The tremendous increase and om- nipresent accessibility of graphic documents on the network led to the high interest in research on content-based image retrieval (CBIR). This has ce- mented the approach for a massive sum of innovative procedures and schemes, and growing curiosity in allied fields to upkeep such projects. Existing associ- ated theories include efficient Content-based Image Retrieval (CBIR) frame by enacting the content- based image, K-means and hybrid clustering is func- tional over combined lineament vector of information images, texture features. In similar cases it is tight in expressing the user’s semantic Intention knowledge to permit information distribution and reuse, models ought to be managed within repositories, where they might be retrieved upon users’ queries. There is still a lack of adequate tools for incisive/handling visual content. In this paper, a novel algorithm Efficient Density-based Clustering Algorithm (EDBC) is sug- gested for content-based image retrieval technique that will enhance scalability and lower maintenance costs significantly, enhance the efficacy of software development.

Two-layer content-based image retrieval technique for improving effectiveness

Two-layer content-based image retrieval technique for improving effectiveness

Material and textural features-based retrieval using feature similarity mapping for synthetic images

Content Based image retrieval (CBIR) is one of the prominent application of image processing where image retrieval from the database is made automatic based on the content in it. The materialist details of the images contains high degree of information and can serve as important toll for analysis. Different variant of content based image retrieval work by locating an object in the image and then retrieving images with similar object in it. The method works using different similarity assessment techniques. In this research work, we have developed a new material feature and HMMD space CBIR technique which works by transforming the domain to HMMD space and then finding the material and Texture Features along with other relevant features. These features forms he feature vector and are then deployed to assess the similarity of the two images which then generates a relevant and irrelevant image search. The system has higher accuracy of assessment as verified by experimentation for precision, productivity and precision agriculture.

Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval.

Recently, content-based image retrieval (CBIR) based on bag-of-visual-words (BoVW) model has been one of the most promising and increasingly active research areas. In this paper, we propose a new CBIR framework based on the visual words fusion of multiple feature descriptors to achieve an improved retrieval performance, where interest points are separately extracted from an image using features from accelerated segment test (FAST) and speeded-up robust features (SURF). The extracted keypoints are then fused together in a single keypoint feature vector and the improved RootSIFT algorithm is applied to describe the region surrounding each keypoint. Afterward, the FeatureWiz algorithm is employed to reduce features and select the best features for the BoVW learning model. To create the codebook, K-means clustering is applied to quantize visual features into a smaller set of visual words. Finally, the feature vectors extracted from the BoVW model are fed into a support vector machines (SVMs) classifier for image retrieval. An inverted index technique based on cosine distance metric is applied to sort the retrieved images to the similarity of the query image. Experiments on three benchmark datasets (Corel-1000, Caltech-10 and Oxford Flower-17) show that the presented CBIR technique can deliver comparable results to other state-of-the-art techniques, by achieving average accuracies of 92.94%, 98.40% and 84.94% on these datasets, respectively.

Keyframe image processing of semantic 3D point clouds based on deep learning.

With the rapid development of web technologies and the popularity of smartphones, users are uploading and sharing a large number of images every day. Therefore, it is a very important issue nowadays to enable users to discover exactly the information they need in the vast amount of data and to make it possible to integrate their large amount of image material efficiently. However, traditional content-based image retrieval techniques are based on images, and there is a "semantic gap" between this and people's understanding of images. To address this "semantic gap," a keyframe image processing method for 3D point clouds is proposed, and based on this, a U-Net-based binary data stream semantic segmentation network is established for keyframe image processing of 3D point clouds in combination with deep learning techniques.

Content-Based Image Retrieval System using Color Moment and Bag of Visual Words with Local Binary Pattern

Researchers have recently focused their attention on Content-Based Image Retrieval (CBIR). It has emerged as one of the most fascinating areas in image processing and computer vision. With CBIR, the most comparable pictures that match the query image are pulled from an image database. As a result, it necessitates feature extraction (Local / Global) and similarity calculation. This paper uses a CBIR technique to determine the images that best match the image query by utilizing both global and local image features. A color moment is used for global features to describe the complete image. Local Binary Pattern (LBP) as a local feature, on the other hand, extracts interest points by building a Bag of Visual Words (BoVW). The distance between the query and database image features is computed using the Euclidean distance. Precision and recall are computed on the Corel-1K dataset to assess the retrieval performance.

A Multi-modal CBIR Framework with Image Segregation using Autoencoders and Deep Learning-based Pseudo-labeling

Various modulated techniques of Content-Based Image Retrieval (CBIR) using deep learning provide better search outputs even though they are computationally challenging. These methods can be enhanced further, if the search key can be tagged effectively and directed towards target images. In this paper, we have developed a new multilevel aggregation technique along with autoencoders, to be implemented on image features for precise feature selection and accurate search output. Locally significant datasets and generic ones are dealt separately for obtaining better hit rates along with the process of query expansion. The concept of pseudo-labelling by deep learning is introduced to classify images into positive and negative classes, where positive class consists of the images that are similar to the query image. The feature spaces of query images are compared with the images in the search pool based on their assigned weights. The target images are finally ranked and selected based on an adaptive threshold level. The methodology for this enhanced CBIR technique is tested on public datasets and its results are collated with recent approaches proposed in the literature. The proposed method has attained significant improvements in precision, recall and computation and thereby the use of images from connected devices has been proven to be significant. Given the importance of more accurate image retrieval in CBIR, this method can be experimented for other similar applications as well.

Fusion of vectored text descriptors with auto extracted deep CNN features for improved image classification

• Transfer learning approach on pre-trained DCNN models for the WANG dataset. • Using word2cev for creating an embedding vector followed by a combination of DSSM and LSTM to reduce the size of a word vector and using these new word vectors to assign five synonyms for each label or keyword for an image to improve classification accuracy. • Fusion of auto extracted features from pre-trained models and word to vector features obtained from word2vec, DSSM, and LSTM for improved content-based image classification (CBIC). In today's age, massive image data is generated rapidly. This influx has made labeling images tedious and, in turn, made it harder to retrieve images through searching algorithms that rely only on labels, keywords, or other meta-data in the images. Modern Content-Based Image Retrieval (CBIR) techniques rely on the visual features within the image to return relevant results to a search query. Deep Convolutional Neural Network (DCNN) models made great strides in the last decade. This paper relies on these complex pre-trained models to extract visual features from images. The proposed work has used pre-trained models like VGG16, MobileNet, Inceptionv3, and Xception for this task. Some studies in the CBIR space also suggest increased accuracy when both visual and textual features are considered. This paper proposes a novel three-step process for obtaining textual features. Firstly, the proposed model receives keywords for each image using Google Cloud Vision API. Secondly, the proposed model replaces each keyword with a 300-dimensional embedding vector obtained using word2vec, trained on the Google News dataset. Finally, the proposed model trains a combination of the Deep Semantic Similarity Model (DSSM) and Long Short-Term Memory (LSTM) model to reduce the 300-dimensional vector to a 64-dimensional vector. Using these new shortened word vectors, the proposed model computes the cosine similarity to replace each keyword of an image with five of its synonyms. Here, these additional steps increased the accuracy compared to simply using a word embedding technique. Finally, the proposed model combined the visual and textual feature vectors and observed that this feature set showed maximum classification accuracy of 98.33%, which is also compared with and found relatively better than other similar model results.

SMPP-CBIR: shorted and mixed aggregated image features for privacy-preserving content-based image retrieval

Thanks to recent breakthroughs in photographic and digital technology, enormous amounts of image data are generated daily. Many content-based image retrieval (CBIR) systems have been developed for searching image collections. However, these systems need more computer and storage resources that can be met by cloud servers, since they supply a lot of processing power at a reasonable price. The protection of users' personal information is a worry for image owners since cloud services are not exactly trustworthy. In this paper, we suggest and put into practice a CBIR (SMPP-CBIR) technique for searching and retrieving ciphertext information that protects security. Asymmetric scalar-product-preserving encryption process (ASPE) is used to preserve aggregated mixed feature vectors while still enabling computation between them to describe the related picture collection. The k-means clustering algorithm is used to recursively arrange all encrypted attributes into a tree index in order to speed up search times. The findings show that SMPP-CBIR is more scalable, more precise, and faster in indexing and retrieval than earlier systems.

An effective bi-layer content-based image retrieval technique

An effective bi-layer content-based image retrieval technique

A hybrid CBIR system using novel local tetra angle patterns and color moment features

The exponential growth of communal media platforms including Facebook and Twitter, and the accessibility of low-cost digital capturing devices have generated an enormous number of multimedia content including images. Effective handling of such massive image collection has boosted the development of content-based image retrieval (CBIR) approaches. Researchers have suggested both machine learning and non-learning-based techniques for CBIR. However, machine learning-based methods outperform the non-learning-based methods in the CBIR domain. The CBIR demands the development of reliable descriptors to attain the most appropriate images from the depository and better address the semantic gap problem. To counter these problems, we suggest a novel second-order Local Tetra Angle Patterns (LTAP) to better capture the texture features from the image. LTAPs are computed from adjacent pixels of 0°, 45°, 90°, and 135° using the second-order directional derivatives. Further, we propose a hybrid feature vector by concatenating LTAPs and RGB color features and using the genetic algorithm (GA) to select the finest appropriate features that enhance the image retrieval performance of our system. We employed our hybrid descriptor with the GA to optimize the support vector machine (SVM) for the image classification task and used the Chi-square quadratic distance measure to determine the resemblance between the query image and the images in the repository. Experimental results on three standard datasets including the Corel 1 k, Oxford flower, and CIFAR-10 indicate the effectiveness of the presented system over the contemporary CBIR methods.

Enhanced Visual Analytics Technique for Content-Based Medical Image Retrieval

Content-based image retrieval (CBIR) is a method for searching that finds related images in a medical database. Furthermore, a clinical adaptation of CBIR is hampered in part by a contextual gap that is the disparity among the person characterization of the picture and the framework characterization of the image. This technique makes it tough for the user to validate the fetched images that are similar to the query image in addition to that it only fetches the images of top-ranked and ignores the low-ranking ones. Visual Analytics for Medical Image Retrieval is a novel procedure for medicinal CBIR proposed in this research (VAMIR). By integrating human and machine analysis, Visual Analytics provides the potential to address the above-mentioned significant challenges. The texture properties are retrieved using the shape features extraction and Gray Level Co-occurrence Matrix (GLCM) is performed by contour-based shape descriptor. Using the Euclidean distance correlation metric, related medical pictures will be fetched by distinguishing the query image's attribute vector with the database images' respective attribute vectors. A vector of multiple features outperforms a vector of a single feature in terms of quality. The VAMIR implementation demonstrates that the search outcome for the user is acquired with 90% of recall and precision.