Explore the vast range of titles available.

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels are required for more complex problems, such as RSIR. This motivated us to present a new benchmark dataset termed \"MLRSIR\" that was labeled from an existing single-labeled remote sensing archive. MLRSIR contained a total of 17 classes, and each image had at least one of 17 pre-defined labels. We evaluated the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep-learning-based ones on MLRSIR. More specifically, we compared the performances of RSIR methods from both single-label and multi-label perspectives. These results presented the advantages of multiple labels over single labels for interpreting complex remote sensing images, and serve as a baseline for future research on multi-label RSIR.

With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.

Most of the existing image steganographic approaches embed the secret information imperceptibly into a cover image by slightly modifying its content. However, the modification traces will cause some distortion in the stego-image, especially when embedding color image data that usually contain thousands of bits, which makes successful steganalysis possible. In this paper, we propose a novel coverless steganographic approach without any modification for transmitting secret color image. In our approach, instead of modifying a cover image to generate the stego-image, steganography is realized by using a set of proper partial duplicates of a given secret image as stego-images, which are retrieved from a natural image database. More specifically, after dividing each database image into a number of non-overlapping patches and indexing those images based on the features extracted from these patches, we search for the partial duplicates of the secret image in the database to obtain the stego-images, each of which shares one or several visually similar patches with the secret image. At the receiver end, by using the patches of the stego-images, our approach can approximately recover the secret image. Since the stego-images are natural ones without any modification traces, our approach can resist all of the existing steganalysis tools. Experimental results and analysis prove that our approach not only has strong resistance to steganalysis, but also has desirable security and high hiding capability.

The problems of image mining and semantic image retrieval play an important role in many areas of life. In this paper, a semantic-based image retrieval system is proposed that relies on the combination of C-Tree, which was built in our previous work, and a neighbor graph (called Graph-CTree) to improve accuracy. The k-Nearest Neighbor (k-NN) algorithm is used to classify a set of similar images that are retrieved on Graph-CTree to create a set of visual words. An ontology framework for images is created semi-automatically. SPARQL query is automatically generated from visual words and retrieve on ontology for semantics image. The experiment was performed on image datasets, such as COREL, WANG, ImageCLEF, and Stanford Dogs, with precision values of 0.888473, 0.766473, 0.839814, and 0.826416, respectively. These results are compared with related works on the same image dataset, showing the effectiveness of the methods proposed here.

Rapid identification of plant diseases is essential for effective mitigation and control of their influence on plants. For plant disease automatic identification, classification of plant leaf images based on deep learning algorithms is currently the most accurate and popular method. Existing methods rely on the collection of large amounts of image annotation data and cannot flexibly adjust recognition categories, whereas we develop a new image retrieval system for automated detection, localization, and identification of individual leaf disease in an open setting, namely, where newly added disease types can be identified without retraining. In this paper, we first optimize the YOLOv5 algorithm, enhancing recognition ability in small objects, which helps to extract leaf objects more accurately; secondly, integrating classification recognition with metric learning, jointly learning categorizing images and similarity measurements, thus, capitalizing on prediction ability of available image classification models; and finally, constructing an efficient and nimble image retrieval system to quickly determine leaf disease type. We demonstrate detailed experimental results on three publicly available leaf disease datasets and prove the effectiveness of our system. This work lays the groundwork for promoting disease surveillance of plants applicable to intelligent agriculture and to crop research such as nutrition diagnosis, health status surveillance, and more.

The proliferation of intelligent sensor networks in urban surveillance and remote sensing has triggered the explosive growth of unstructured visual sensor data. Accurately retrieving targets from these massive streams based on complex cross-modal user intents remains a critical bottleneck for efficient intelligent perception. Composed Image Retrieval (CIR) addresses this by enabling retrieval via a multi-modal query that combines a reference image with semantic control signals. However, existing methods often struggle with abstract instructions in real-world scenarios. Consequently, models often suffer from feature distribution shifts due to focus ambiguity, as well as semantic erosion caused by highly entangled visual and textual features. To address these challenges, we propose a geometry-based Selective Orthogonal Projection Network (SOP). First, the Selective Focus Recovery module quantifies instruction uncertainty via information entropy and calibrates shifted query features to the true target distribution using structural consistency regularization. Second, to ensure data fidelity, we introduce Orthogonal Subspace Projectionand Geometric Composition Fidelity. These mechanisms employ Gram–Schmidt orthogonalization to decouple features into a constant visual base and an orthogonal modification increment, restricting semantic modifications to the null space. Extensive experiments on FashionIQ, Shoes, and CIRR datasets demonstrate that SOP significantly outperforms SOTA methods, offering a novel solution for efficient large-scale sensor data retrieval and analysis.

Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.

Content-based image retrieval (CBIR) is essential for managing and searching massive image repositories across a wide variety of applications. Nevertheless, some traditional CBIR systems exhibit low retrieval accuracy because they use predetermined feature weights, lack semantic gaps, and poorly exploit heterogeneous visual features. To overcome such difficulties, the present study will introduce a multi-feature adaptive CBIR framework that combines deep and handcrafted features using an information entropy-based fusion and a trust-based weighting system. Deep convolutional models, combined with complementary low-level descriptors, are used to extract discriminative features in the proposed approach. A PageRank-based similarity propagation strategy is also used to narrow image ranking by leveraging similarity relationships across the globe. Evaluation is performed using standard retrieval measures, such as Mean Average Precision (mAP), Precision at K, Recall at K, and NDCG. The experimental results show that the proposed approach consistently improves performance across benchmark datasets. The framework boosts mAP by up to 8.6% over traditional fixed-weight fusion methods, while Precision@10 and NDCG@10 increase by 6.2% and 7.4%, respectively. The statistical analysis shows that these improvements are significant at the 95% confidence level, indicating that retrieval behavior is robust and reliable. These findings confirm the efficiency of entropy-driven adaptive fusion and ranking refinement in overcoming the major drawbacks of current CBIR systems, and the suggested framework is appropriate for large-scale image search in practice.

Cross-modal retrieval requires building a common latent space that captures and correlates information from different data modalities, usually images and texts. Cross-modal training based on the triplet loss with hard negative mining is a state-of-the-art technique to address this problem. This paper shows that such approach is not always effective in handling intra-modal similarities. Specifically, we found that this method can lead to inconsistent similarity orderings in the latent space, where intra-modal pairs with unknown ground-truth similarity are ranked higher than cross-modal pairs representing the same concept. To address this problem, we propose two novel loss functions that leverage intra-modal similarity constraints available in a training triplet but not used by the original formulation. Additionally, this paper explores the application of this framework to unsupervised image retrieval problems, where cross-modal training can provide the supervisory signals that are otherwise missing in the absence of category labels. Up to our knowledge, we are the first to evaluate cross-modal training for intra-modal retrieval without labels. We present comprehensive experiments on MS-COCO and Flickr30k, demonstrating the advantages and limitations of the proposed methods in cross-modal and intra-modal retrieval tasks in terms of performance and novelty measures. We also conduct a case study on the ROCO dataset to assess the performance of our method on medical images and present an ablation study on one of our approaches to understanding the impact of the different components of the proposed loss function. Our code is publicly available on GitHub https://github.com/MariodotR/FullHN.git .

In medical applications, retrieving similar images from repositories is most essential for supporting diagnostic imaging-based clinical analysis and decision support systems. However, this is a challenging task, due to the multi-modal and multi-dimensional nature of medical images. In practical scenarios, the availability of large and balanced datasets that can be used for developing intelligent systems for efficient medical image management is quite limited. Traditional models often fail to capture the latent characteristics of images and have achieved limited accuracy when applied to medical images. For addressing these issues, a deep neural network-based approach for view classification and content-based image retrieval is proposed and its application for efficient medical image retrieval is demonstrated. We also designed an approach for body part orientation view classification labels, intending to reduce the variance that occurs in different types of scans. The learned features are used first to predict class labels and later used to model the feature space for similarity computation for the retrieval task. The outcome of this approach is measured in terms of error score. When benchmarked against 12 state-of-the-art works, the model achieved the lowest error score of 132.45, with 9.62–63.14% improvement over other works, thus highlighting its suitability for real-world applications.