Traditional Image Retrieval Methods Research Articles

Performance evaluation of attention-deep hashing based medical image retrieval in brain MRI datasets

BackgroundBrain MRI images pose significant challenges due to their complexity and voluminous data, which often hinder the accuracy of traditional image retrieval methods. In response, this research delves into a novel medical image retrieval approach grounded in attention mechanisms and deep hashing techniques. MethodologyThe study enhances the network's edge perception capability by incorporating a dual mixed attention module (CBAM-MA dual attention) into the convolutional neural network architecture. This addition optimally captures edge saliency and distributional features by introducing an extra attention layer atop CBAM. Furthermore, a novel triple loss function is introduced, amalgamating Euclidean and cosine distances. This fusion exploits the strengths of both distance metrics to comprehensively consider sample geometric attributes, thereby generating more robust and expressive feature representations crucial for preserving categorical details. ResultsExperimental evaluations showcase significant performance advancements in MRI image retrieval tasks using the proposed method. The average hit rate stands at 0.7783, average precision at 0.752246, and average inverse rank at 0.956721. These metrics collectively underscore the method's efficacy in enhancing the quality and expediting the diagnosis of brain diseases. ConclusionThe research underscores the critical role of attention and deep hashing techniques in augmenting the accuracy and efficiency of medical image retrieval, particularly in the context of brain MRI images. The proposed method's notable performance improvements pave the way for enhanced diagnostic capabilities, signaling a significant step forward in improving healthcare outcomes related to brain diseases.

An Image Retrieval Method for Lunar Complex Craters Integrating Visual and Depth Features

In the geological research of the Moon and other celestial bodies, the identification and analysis of impact craters are crucial for understanding the geological history of these bodies. With the rapid increase in the volume of high-resolution imagery data returned from exploration missions, traditional image retrieval methods face dual challenges of efficiency and accuracy when processing lunar complex crater image data. Deep learning techniques offer a potential solution. This paper proposes an image retrieval model for lunar complex craters that integrates visual and depth features (LC2R-Net) to overcome these difficulties. For depth feature extraction, we employ the Swin Transformer as the core architecture for feature extraction and enhance the recognition capability for key crater features by integrating the Convolutional Block Attention Module with Effective Channel Attention (CBAMwithECA). Furthermore, a triplet loss function is introduced to generate highly discriminative image embeddings, further optimizing the embedding space for similarity retrieval. In terms of visual feature extraction, we utilize Local Binary Patterns (LBP) and Hu moments to extract the texture and shape features of crater images. By performing a weighted fusion of these features and utilizing Principal Component Analysis (PCA) for dimensionality reduction, we effectively combine visual and depth features and optimize retrieval efficiency. Finally, cosine similarity is used to calculate the similarity between query images and images in the database, returning the most similar images as retrieval results. Validation experiments conducted on the lunar complex impact crater dataset constructed in this article demonstrate that LC2R-Net achieves a retrieval precision of 83.75%, showcasing superior efficiency. These experimental results confirm the advantages of LC2R-Net in handling the task of lunar complex impact crater image retrieval.

Pre-trained convolution neural networks models for content-based medical image retrieval

Content-based image retrieval (CBIR) is a recent method used to retrieve different types of images from repositories. The traditional content-based medical image retrieval (CBMIR) methods commonly used low-level image representation features extracted from color, texture, and shape image descriptors. Since most of these CBMIR systems depend mainly on the extracted features, the methods used in the feature extraction phase are more important. Features extraction methods, which generate inaccurate features, lead to very poor performance retrieval because of semantic gap widening. Hence, there is high demand for independent domain knowledge features extraction methods, which have automatic learning capabilities from input images. Pre-trained deep convolution neural networks (CNNs), the recent generation of deep learning neural networks, could be used to extract expressive and accurate features. The main advantage of these pre-trained CNNs models is the pre-training process for huge image data of thousands of different classes, and their knowledge after the training process could easily be transferred. There are many successful models of pre-trained CNNs models used in the area of medical image retrieval, image classification, and object recognition. This study utilizes two of the most known pre-trained CNNs models; ResNet18 and SqueezeNet for the offline feature extraction stage. Additionally, the highly accurate features extracted from medical images are used for the CBMIR method of medical image retrieval. This study uses two popular medical image datasets; Kvasir and PH2 to show that the proposed methods have good retrieval results. The retrieval performance evaluation measures of our proposed method have average precision of 97.75% and 83.33% for Kvasir and PH2 medical images respectively, and outperform some of the state-of-the-art methods in this field of study because these pre-trained CNNs have well trained layers among a huge number of image types. Finally, intensive statistical analysis shows that the proposed ResNet18-based retrieval method has the best performance for enhancing both recall and precision measures for both medical images.

Weakly Supervised Class-Agnostic Image Similarity Search Based on Convolutional Neural Network

For vision-based localization methods, the efficient and accurate image retrieval in a database with a large number of images is an important prerequisite and guarantee. The traditional content-based image retrieval method can retrieve database images that have similar details and features with the query image. However, there may be significant differences in semantics between them, which may result in mismatches. The method based on convolutional neural network can avoid such problems to a certain extent. In this paper, we propose an end-to-end weakly supervised class-agnostic image retrieval method based on convolutional neural networks. In the offline stage, the database images are preprocessed to separate the foregrounds and backgrounds, and the foregrounds are clustered for storage. The aim is to reduce the amount of data calculation in the online stage and avoid mismatches caused by backgrounds mixing. In the online stage, the same processing is performed on the query image, and the improved method based on the relational network is used to compare the similarity between the input objects to obtain the optimal retrieval result. The test results on Holidays, Paris 6k, Oxford 5k and three large-scale datasets show that the proposed method has better performance than existing image retrieval methods.

Image Retrieval of Tourism Landscape in Rural Revitalization Based on Wireless Communication Network

With the rapid development of information technology, people's acquisition of tourism and other information is increasingly dependent on images and other information. Aiming at the low efficiency of traditional image retrieval methods in processing massive image data, an image retrieval method based on wireless communication network is proposed. Based on the salient area and wireless communication network combined with the hash method to extract local CNN features, simulate INRIA Holidays Data set and Oxford Buildings Data set, and calculate the accuracy and recall rate of search results based on the returned results of tourist attractions pictures. This article designs an experiment to verify the accuracy and recall rate of search results. By comparing the feature hash function to generate the hash code and the Hamming distance between each hash code in the image library, the image is queried, and the final search result is obtained: The more searches, the lower the accuracy and recall rate. This also proves to a certain extent that the CNN feature extraction technology can be used for travel image search, improving the search accuracy by 20%.The wireless communication network is still of great significance to the future social development. It is necessary to conduct in-depth research, not only the image retrieval of tourist attractions proposed in this article but also the potential value of wireless communication networks from multiple angles and more comprehensively.

DenseHashNet: A Novel Deep Hashing for Medical Image Retrieval

With the wide application of imaging modalities such as X-ray and Computed Tomography (CT) in clinical practice, Content-based Medical Image Retrieval (CBMIR) has become a current research hotspot. Related studies have shown that hash-based image retrieval algorithms can retrieve relevant images faster and more accurately than traditional image retrieval methods. Therefore, in this paper, we propose a novel deep hashing method for medical image retrieval, called DenseHashNet. Specifically, we first use DenseNet to extract the original image features, and introduce the Spatial Pyramid Pooling (SPP) layer after the last Dense Block so that features at different scales can be extracted and multi-scale features fused with information from multiple regions. Then, the output of the SPP layer is subjected to Power-Mean Transformation (PMT) operation to enhance the nonlinearity of the model and improve the performance of the model. Finally, we map the output of PMT to hash codes through fully connected layers. Experimental results show that our method achieves better performance, compared with some representative methods.

A multi-scale UAV image matching method applied to large-scale landslide reconstruction.

Three-dimensional (3D) sparse reconstruction of landslide topography based on unmanned aerial vehicle (UAV) images has been widely used for landslide monitoring and geomorphological analysis. In order to solve the isolated island phenomenon caused by multi-scale image matching, which means that there is no connection between the images of different scales, we herein propose a method that selects UAV image pairs based on image retrieval. In this method, sparse reconstruction was obtained via the sequential structure-from-motion (SfM) pipeline. First, principal component analysis (PCA) was used to reduce high-dimensional features to low-dimensional features to improve the efficiency of retrieval vocabulary construction. Second, by calculating the query depth threshold and discarding the invalid image pairs, we improved the efficiency of image matching. Third, the connected network of the dataset was constructed based on the initial matching of image pairs. The lost multi-scale image pairs were identified and matched through the image query between the connection components, which further improved the integrity of image matching. Our experimental results show that, compared with the traditional image retrieval method, the efficiency of the proposed method is improved by 25.9%.

Deep Multiple Instance Hashing for Fast Multi-Object Image Search.

Multi-keyword query is widely supported in text search engines. However, an analogue in image retrieval systems, multi-object query, is rarely studied. Meanwhile, traditional object-based image retrieval methods often involve multiple steps separately. In this work, we propose a weakly-supervised Deep Multiple Instance Hashing (DMIH) approach for multi-object image retrieval. Our DMIH approach, which leverages a popular CNN model to build the end-to-end relation between a raw image and the binary hash codes of its multiple objects, can support multi-object queries effectively and integrate object detection with hashing learning seamlessly. We treat object detection as a binary multiple instance learning (MIL) problem and such instances are automatically extracted from multi-scale convolutional feature maps. We also design a conditional random field (CRF) module to capture both the semantic and spatial relations among different class labels. For hashing training, we sample image pairs to learn their semantic relationships in terms of hash codes of the most probable proposals for owned labels as guided by object predictors. The two objectives benefit each other in a multi-task learning scheme. Finally, a two-level inverted index method is proposed to further speed up the retrieval of multi-object queries. Our DMIH approach outperforms state-of-the-arts on public benchmarks for object-based image retrieval and achieves promising results for multi-object queries.

Efficient retrieval algorithm for multimedia image information

The research on the retrieval of multimedia image data information is of great significance for increasing the retrieval rate of multimedia image information. Due to the certain similar characteristics of massive multimedia image information, the picture information features are confused. The traditional image retrieval method mainly uses the image information feature to classify and retrieve. When the picture information is disordered, it is impossible to classify the mass multimedia image information features, resulting in slow retrieval speed and low accuracy. A new high-efficiency retrieval algorithm for massive multimedia image information is proposed and optimized. Based on the theory of granular computing, an image region similarity measurement method for content retrieval is proposed. The image feature information table is transformed into an ordered matrix form. By studying the ordered matrix, the concept of image feature granules and granule granules is introduced, the importance of image features is analyzed from different granularity levels, and the order relationship between regions in the image feature information table is maintained, and the weight of the theoretical image feature is calculated based on the granularity for implementing the image region similarity measurement method. The example shows that the similarity measure method can measure the degree of similarity between image regions objectively and effectively, and provides a new idea and method for the research of granular computing theory in multimedia image content retrieval.

CATIRI: An Efficient Method for Content-and-Text Based Image Retrieval

The combination of visual and textual information in image retrieval remarkably alleviates the semantic gap of traditional image retrieval methods, and thus it has attracted much attention recently. Image retrieval based on such a combination is usually called the content-and-text based image retrieval (CTBIR). Nevertheless, existing studies in CTBIR mainly make efforts on improving the retrieval quality. To the best of our knowledge, little attention has been focused on how to enhance the retrieval efficiency. Nowadays, image data is widespread and expanding rapidly in our daily life. Obviously, it is important and interesting to investigate the retrieval efficiency. To this end, this paper presents an efficient image retrieval method named CATIRI (content-and-text based image retrieval using indexing). CATIRI follows a three-phase solution framework that develops a new indexing structure called MHIM-tree. The MHIM-tree seamlessly integrates several elements including Manhattan Hashing, Inverted index, and M-tree. To use our MHIM-tree wisely in the query, we present a set of important metrics and reveal their inherent properties. Based on them, we develop a top-k query algorithm for CTBIR. Experimental results based on benchmark image datasets demonstrate that CATIRI outperforms the competitors by an order of magnitude.

Instance-level object retrieval via deep region CNN

Instance retrieval is a fundamental problem in the multimedia field for its various applications. Since the relevancy is defined at the instance level, it is more challenging comparing to traditional image retrieval methods. Recent advances show that Convolutional Neural Networks (CNNs) offer an attractive method for image feature representations. However, the CNN method extracts features from the whole image, thus the extracted features contain a large amount of background noisy information, leading to poor retrieval performance. To solve the problem, this paper proposed a deep region CNN method with object detection for instance-level object retrieval, which has two phases, i.e., offline Faster R-CNN training and online instance retrieval. First, we train a Faster R-CNN model to better locate the region of the objects. Second, we extract the CNN features from the detected object image region and then retrieve relevant images based on the visual similarity of these features. Furthermore, we utilized three different strategies for feature fusing based on the detected object region candidates from Faster R-CNN. We conduct the experiment on a large dataset: INSTRE with 23,070 object images and additional one million distractor images. Qualitative and quantitative evaluation results have demonstrated the advantage of our proposed method. In addition, we conducted extensive experiments on the Oxford dataset and the experimental results further validated the effectiveness of our proposed method.

Fast BGP Face Retrieval Based on K-means Clustering

The traditional face image retrieval method is to compare the target picture with all the pictures in the database one by one, resulting in great time consumption. This paper used the binary gradient pattern (BGP) as face feature extractor. Firstly, the database was encoded offline. Then the encoded database was offline clustered. Next, we extracted the feature of the target picture by BGP and found the nearest clustering centre from the database encoding. Finally, the target picture code for retrieval was selected in the target class.

A novel biomedical image indexing and retrieval system via deep preference learning

Background and ObjectivesThe traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. MethodsWe exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. ResultsWe evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. ConclusionsWe propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications.

A Novel Web Image Retrieval Method

Traditional image retrieval methods, make use of color, shape and texture features, are based on local image database. But in the condition of which much more images are available on the internet, so big an image database includes various types of image information. In this paper, we introduce an intellectualized image retrieval method based on internet, which can grasp images on Internet automatically using web crawler and build the feature vector in local host. The method involves three parts: the capture-node, the manage-node, and the calculate-node. The calculate-node has two functions: feature extract and similarity measurement. According to the results of our experiments, we found the proposed method is simple to realization and has higher processing speed and accuracy.

An Automatic Accurate High-Resolution Satellite Image Retrieval Method

With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method based on effective coverage (EC) information, which is used to replace the artificial screening stage in traditional satellite image retrieval tasks. In this method, first, we use a convolutional neural network to extract the EC of each satellite image; then, we use an effective coverage grid set (ECGS) to represent the ECs of all satellite images in the library; finally, the satellite image accurate retrieval algorithm is proposed to complete the process of screening images. The performance evaluation of the method is implemented in three regions: Wuhan, Yanling, and Tangjiashan Lake. The large number of experiments shows that our proposed method can automatically retrieve high-resolution satellite images and significantly improve efficiency.

Image Retrieval Using Fused Deep Convolutional Features

This paper proposes an image retrieval using fused deep convolutional features to solve the semantic gap between low-level features and high-level semantic features of traditional contend-based image retrieval method. Firstly, the improved network architecture LeNet-L is obtained by improving convolutional neural network LeNet-5. Then, fusing two different deep convolutional features which are extracted by LeNet-5 and AlexNet. Finally, after the fusion, the similar image is obtained through comparing the similarity between the image being retrieved and the image in database by distance function. In Corel dataset, this method is compared with the single convolutional neural network extracted features for image retrieval method, it has a higher precision and recall. The results show that this method has a better retrieval accuracy.

Research on New Multi-Feature Large-Scale Image Retrieval Algorithm based on Semantic Parsing and Modified Kernel Clustering Method

Because of the feature points can describe the local characteristics of the image in a reasonable manner, effective use of feature point of content based image retrieval become the current hot issues in the field of computer vision. Aiming at this problem, we put forward a kind of combination clustering based on feature points, a new method of image retrieval. The method includes the combination of feature point clustering algorithm and based on the algorithm of local color histogram construction strategy. With the existing and local color histogram retrieval method based on feature points, compared to the method can effectively solve the current method of feature point location information and feature point center relying too much on the problem. Subjectivity and as a result of the manual annotation image accuracy, the traditional image retrieval methods cannot meet the needs of the user. Multidimensional indexing technology is only from the perspective of how to improve the indexing algorithm to adapt to the large-scale database to consider a problem, in content-based image retrieval. Our research combines the advantages of the semantic analysis and kernel clustering which will enhance the performance of the traditional image retrieval methods and strengthen the feasibility of the algorithm.

A New Approach for Large-Scale Scene Image Retrieval Based on Improved Parallelk-Means Algorithm in MapReduce Environment

The rapid growth of digital images has caused the traditional image retrieval technology to be faced with new challenge. In this paper we introduce a new approach for large-scale scene image retrieval to solve the problems of massive image processing using traditional image retrieval methods. First, we improved traditionalk-Means clustering algorithm, which optimized the selection of the initial cluster centers and iteration procedure. Second, we presented a parallel design and realization method for improvedk-Means algorithm applied it to feature clustering of scene images. Finally, a storage and retrieval scheme for large-scale scene images was put forward using the large storage capacity and powerful parallel computing ability of the Hadoop distributed platform. The experimental results demonstrated that the proposed method achieved good performance. Compared with the traditional algorithms with single node architecture and parallelk-Means algorithm, the proposed method has obvious advantages for use in large-scale scene image data retrieval in terms of retrieval accuracy, retrieval time overhead, and computational performance (speedup and efficiency, sizeup, and scaleup), which is a significant improvement from applying parallel processing to intelligent algorithms with large-scale datasets.

Enhancing Sketch-Based Image Retrieval by Re-Ranking and Relevance Feedback.

A sketch-based image retrieval often needs to optimize the tradeoff between efficiency and precision. Index structures are typically applied to large-scale databases to realize efficient retrievals. However, the performance can be affected by quantization errors. Moreover, the ambiguousness of user-provided examples may also degrade the performance, when compared with traditional image retrieval methods. Sketch-based image retrieval systems that preserve the index structure are challenging. In this paper, we propose an effective sketch-based image retrieval approach with re-ranking and relevance feedback schemes. Our approach makes full use of the semantics in query sketches and the top ranked images of the initial results. We also apply relevance feedback to find more relevant images for the input query sketch. The integration of the two schemes results in mutual benefits and improves the performance of the sketch-based image retrieval.

Semantic Concept and Weighted Visual Feature Based Image Retrieval

The performance of content-based image retrieval is degraded because of the existence of the semantic gap. In order to address this drawback, this paper proposes an image retrieval method fusing with the semantic concept and the weighted visual feature. In the approach, each image is segmented using the normalized cut (N-cut), and the visual characteristics are extracted. After that, the semantic concept is achieved based on the mapping from keywords to image low-level characteristics. The distinctive proportion of the same concept in different images may lead to the priority retrieval of the image which has the same similarity and smaller correlated regions with others. Therefore, we use the weighted visual features to sort the retrieved images. Extensive experiments show the retrieval performance of the proposed method is superior to the traditional content-based image retrieval methods.