Data-efficient generalization for zero-shot composed image retrieval

Image retrieval is a critical task in many fields such as indexing and Content-Based Image Retrieval is a tried and tested method for image retrieval. With advances in graphics and visualization techniques, image retrieval should be easy, quick and flexible. The aim of this work is to develop an efficient image indexing and retrieval system to collate similar images or images based on conditioned queries in the form of an image itself. The indexing method has been performed using wavelet transform and related mechanisms. Its detailing can easily entail description from a broad range to a narrow range. The indexing algorithm applies a Haar Wavelet-Based Perceptual Similarity Index (HaarPSI) for Image Quality Assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. Results show that the algorithm used is able to retrieve and display images with high retrieval accuracy and medium relevance.The proposed methodology has been tested against known image retrieval techniques and was found to be comparable and in some cases better than most prior known techniques. The testing was performed on images of varied types, from multiple datasets and sizes. The accuracy was found to be 99% in most cases. All in all, this study introduces a simple and convenient way of offline image searches on desktop computers and provides a stepping stone to future content-based image retrieval systems built for similar purposes.

One of the main challenges in medical image retrieval is the increasing volume of image data, which render it difficult for domain experts to find relevant information from large data sets. Effective and efficient medical image retrieval systems are required to better manage medical image information. Text‐based image retrieval (TBIR) was very successful in retrieving images with textual descriptions. Several TBIR approaches rely on models based on bag‐of‐words approaches, in which the image retrieval problem turns into one of standard text‐based information retrieval; where the meanings and values of specific medical entities in the text and metadata are ignored in the image representation and retrieval process. However, we believe that TBIR should extract specific medical entities and terms and then exploit these elements to achieve better image retrieval results. Therefore, we propose a novel reranking method based on medical‐image‐dependent features. These features are manually selected by a medical expert from imaging modalities and medical terminology. First, we represent queries and images using only medical‐image‐dependent features such as image modality and image scale. Second, we exploit the defined features in a new reranking method for medical image retrieval. Our motivation is the large influence of image modality in medical image retrieval and its impact on image‐relevance scores. To evaluate our approach, we performed a series of experiments on the medical ImageCLEF data sets from 2009 to 2013. The BM25 model, a language model, and an image‐relevance feedback model are used as baselines to evaluate our approach. The experimental results show that compared to the BM25 model, the proposed model significantly enhances image retrieval performance. We also compared our approach with other state‐of‐the‐art approaches and show that our approach performs comparably to those of the top three runs in the official ImageCLEF competition.

Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval. The code of this model will be publicly available at1.

Feature extraction and similarity measure are two basic key issues in image retrieval. Combining the advantages of SNN in image recognition and selective attention for image retrieval, a novel visual keywords-driven image retrieval approach based on these properties has been proposed. By using a predefined set of visual keywords as prototype patterns stored with the SNN and then measuring the degree of similiarity of the stored images to the visual keywords, we show that such a visual keyword driven SNN can provide the framework for image indexing or retrieval which is scalable, robust and efficient for web-based search.

In recent years, secure image archival and retrieval model in cloud computing (CC) received significant attention to ensure data confidentiality and secured data transmission among cloud server and clients cloud storage and users. Traditional secure image retrieval (IR) techniques are not applicable to adapt with a large-scale IR in cloud environment. In order to overcome these issues, this article introduces an effective secure IR scheme using Inception with ResNet v2 (SIRS-IR) and Multiple Share Creation (MSC) scheme. The proposed method uses Inception with ResNet v2 model based feature extraction process. At the same time, the MSC process is utilized for multiple share generation and then encryption of shares takes place using double chaotic logistic map (DLCM) technique. Besides, the encrypted shares and the respective feature vectors are saved in the cloud server with the corresponding image identification number. During the IR process for the applied QI, the SIRS-IR model extracts the feature vectors and performs similarity measurement to retrieve the related images from the database interms of encrypted shares. Finally, the share decryption process is carried out for the reconstruction of original images with no loss of quality. Extensive experimentations were performed to verify the retrieval performance and image quality of the reconstructed images using Corel10K dataset. The obtained results stated that the presented SIRS-IR model is found to be superior to other methods in a considerable way.

Image Indexing and Retrieval Using Object-based Point Feature Maps

Different types of search engines are used to search image and text contents. Two types of image search methods are available in the Internet. They are query keyword based model and content based image retrieval models. Text query strings are used in the textual image retrieval model. Content based image retrieval (CBIR) model uses the visual information of the images. Image search methods use the text annotation and image visual features. Google image search and Bing image search engines are used to fetch images from the web. Image query string is used to search image on Internet. One click query image selection method is used to submit user intention for image retrieval. Content based image re-ranking is performed with visual and textual similarity metrics. Adaptive Weight Schema is used for the similarity analysis. Feature weight learning algorithm is applied to estimate feature weights for the images and its category. Query is expanded with keyword and visual information. Rank boost framework algorithm is enhanced to rank images with photographic quality. Content similarity and visual quality factors are used for the re-ranking process. In this paper, we propose an image indexing and retrieval using speech annotations based on a predefined structured syntax. To improve the retrieval effectively, N-best lists for index generation is used .so, a query expansion technique is explored to enhance the query terms. All this process is automatic, without extra effort from the user. This is critically important in web-based image search engine for any commercial, where the user interface has to be extremely simple.

Many different approaches have been taken, some focusing on highly specific topics such as improved pattern-matching algorithms, others taking a much broader view, and attempting to study the issues involved by developing and evaluating prototype systems. Work on image retrieval at Northumbria University falls mainly into this second category. Our long-term aim is to investigate the principles on which workable shape retrieval systems can be constructed. Our approach has been essentially pragmatic to develop a series of steadily more sophisticated prototypes capable of handling an increasingly broad range of image types and retrieval paradigms. Our first project, the development and evaluation of SAFARI (Shape Analysis For Automatic Retrieval of Images), a prototype shape retrieval system for two dimensional engineering drawings of simple machined parts, demonstrated that we could achieve excellent retrieval performance within a restricted class of images. The aim of our current ARTISAN (Automatic Retrieval of Trademark Images by Shape ANalysis) project is twofold: to develop and evaluate a prototype shape retrieval system for trade mark images consisting of abstract geometric designs, and to assess the feasibility of generalizing the image retrieval approach developed in our earlier SAFARI system to a wider range of image types. It is hoped that the prototype system developed will provide a basis from which the Patent Office Trade Marks Registry can develop an improved image retrieval system for their own use.

There is a growing trend for using content‐based image retrieval (CBIR) systems these days because of the constantly growing interest in digital content. Therefore, the ability of the CBIR to perform the CBIR process will depend on the feature extraction process and its basis, for the retrieval will be done on. Numerous researchers put forward various techniques for feature extraction to enhance the nature of the system. Since features play a very key role in enhancing performance, various features can be used collectively to attain the requisite goal. To retain this in mind, we present in this paper a multifeature fusion system, where three features are integrated and form one feature to improve the situation of retrieval. For this purpose, scale‐invariant feature transform (SIFT), speeded‐up robust features (SURF), and histogram of oriented gradients (HOG) features are adopted. These features are common features that deliver information about the shape of the object and for matching purposes, two techniques of distance matching such as Euclidean and Hausdrauff distance are adopted. To assess the performance of the proposed multifeature‐based CBIR approach, experiments were conducted with the usage of a MATLAB simulator. The Corel‐1000 dataset, consisting of 10,000 images in 100 semantic classes, turned into applied, with each magnificence containing 100 images. A subset of 2500 images across 50 semantic classes was used to train the system. This research aligns with industry, innovation, and infrastructure by contributing to advancements in image processing and retrieval systems. Key characteristic descriptors, along with SIFT, SURF, HOG, texture, and multicharacteristic combinations, were extracted for retrieval functions. The results display that the usage of the Hausdrauff distance as a similarity degree outperforms Euclidean distance, accomplishing retrieval accuracies of 80.02% for HOG, 77.9% for SIFT, 79. 8% for SURF, 77.2% for texture, and 84.2% for multicharacteristic combinations, surpassing Euclidean distance results via 1.7%–3.6% across capabilities. These findings underscore the effectiveness of Hausdrauff distance in enhancing retrieval precision within the CBIR framework.

Several Deep Learning (DL) and medical image Machine Learning (ML) methods have been investigated for efficient data representations of medical images, such as image classification, Content-Based Image Retrieval (CBIR), and image segmentation. CBIR helps medical professionals make decisions by retrieving similar cases and images from electronic medical image databases. CBIR needs expressive data representations for similar image identification and knowledge discovery in massive medical image databases explored by distinct algorithmic methods. In this study, an Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval (IWOADL-RFIGR) approach was developed. The presented IWOADL-RFIGR method mainly focused on retrieving and classifying retinal fundus images. The proposed IWOADL-RFIGR method used the Bilateral Filtering (BF) method to preprocess the retinal images, a lightweight Convolutional Neural Network (CNN) based on scratch learning with Euclidean distance-based similarity measurement for image retrieval, and the Least Square Support Vector Machine (LS-SVM) model for image classification. Finally, the IWOA was used as a hyperparameter optimization technique to improve overall performance. The experimental validation of the IWOADL-RFIGR model on a benchmark dataset exhibited better performance than other models.

In this article, we propose a novel system for feature selection, which is one of the key problems in content-based image indexing and retrieval as well as various other research fields such as pattern classification and genomic data analysis. The proposed system aims at enhancing semantic image retrieval results, decreasing retrieval process complexity, and improving the overall system usability for end-users of multimedia search engines. Three feature selection criteria and a decision method construct the feature selection system. Two novel feature selection criteria based on inner-cluster and intercluster relations are proposed in the article. A majority voting-based method is adapted for efficient selection of features and feature combinations. The performance of the proposed criteria is assessed over a large image database and a number of features, and is compared against competing techniques from the literature. Experiments show that the proposed feature selection system improves semantic performance results in image retrieval systems.

Objectives: The objective of the examined active salient component classifier is to extract the image features from the visual substance through a novel non-parametric example based positioning methodology to utilizing comparability and learning strategies. Methods/Statistical Analysis: To establish the authenticity to avoid improper retrieval on online based image classification system called, lite Dynamic Pattern Extraction Algorithm (DPEA) for Image Classification and salient image extraction algorithm is designed. DPEA Algorithm on each benchmark dataset works on user query basis and reducing the overhead incurred during user query processing by applying labeling process. Next, by retrieving salient features on images with zero mean for each client query and each image retrieval reduces the execution time and complexity as the database does not maintain the related features. Finally, the Potential image Classification and retrieval prevents the unauthorized user modification on image data, therefore improving the reality. Here a Web Image Dataset (NUS-WIDE) created by lab of media search in National University of Singapore. The computer vision CIFAR-10 dataset, Modified National Institute of Standards and Technology database of USA (MNIST) dataset is used for experiment. A series of simulation results are performed to test the image retrieval efficiency, execution time, performance fitness for obtaining efficiency of image data handling and measure the effectiveness of DPEA Algorithm. Findings: The tests were directed on the dataset for image classification and retrieval on each region of the images. The Proposed DPEA offers better performance with an improvement of the data confidentiality by 18.72%, reduces execution complexity by 7.98%, reduces average retrieval overhead by 6.65% and also minimize retrieval complexity by 15.57% compared to existing algorithms of LFDA, PCA and PCCA respectively. Application/Improvements: It can be further extended with implementation of new retrieval techniques with different parameters which improves more retrieval efficiency and performance integrity.

Content-based image retrieval (CBIR) techniques will be part of the next generation of search engines. However, even with a suitable retrieval method, real-time image retrieval from a large image database still requires extremely high computational power. In this paper, a Web-based image database indexing and retrieval system is described. It uses fast CBIR with Beowulf-Class parallel computing engine (Super Abacus) to efficiently and effectively shorten the retrieval time. Content-based image retrieval with several parallel algorithms for images which query different color spaces and the compressed-domain of the images are developed to achieve higher efficiency. Text-based query is also supported for images using a conventional annotation approach.

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for Image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR.

Handwritten signature retrieval and identification