Speed Of Image Retrieval Research Articles

Efficient Image Retrieval Using Hierarchical K-Means Clustering.

The objective of content-based image retrieval (CBIR) is to locate samples from a database that are akin to a query, relying on the content embedded within the images. A contemporary strategy involves calculating the similarity between compact vectors by encoding both the query and the database images as global descriptors. In this work, we propose an image retrieval method by using hierarchical K-means clustering to efficiently organize the image descriptors within the database, which aims to optimize the subsequent retrieval process. Then, we compute the similarity between the descriptor set within the leaf nodes and the query descriptor to rank them accordingly. Three tree search algorithms are presented to enable a trade-off between search accuracy and speed that allows for substantial gains at the expense of a slightly reduced retrieval accuracy. Our proposed method demonstrates enhancement in image retrieval speed when applied to the CLIP-based model, UNICOM, designed for category-level retrieval, as well as the CNN-based R-GeM model, tailored for particular object retrieval by validating its effectiveness across various domains and backbones. We achieve an 18-times speed improvement while preserving over 99% accuracy when applied to the In-Shop dataset, the largest dataset in the experiments.

Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization.

In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.

Research Techniques Made Simple: Noninvasive Imaging Technologies for the Delineation of Basal Cell Carcinomas

In this article we discuss the development of noninvasive imaging modalities to help delineate tumor margins of basal cell carcinomas in the setting of Mohs micrographic surgery. A review of the available literature reveals that dermoscopy can help delineate basal cell carcinomas before surgical removal but that it has no benefit over clinical inspection in reducing the number of Mohs stages. In contrast, fluorescence confocal microscopy has a sensitivity of 88-96% and specificity of 89-99% for the detection of basal cell carcinomas and can potentially serve as a rapid means for tumor evaluation on exvivo specimens. Optical coherence tomography has shown some success in the presurgical evaluation of tumor margins invivo, before surgical excision. With ongoing developments in device portability, speed of image retrieval, and image resolution, these technologies are likely to gain traction in cutaneous oncology research and practice. It is therefore important for dermatology clinicians and researchers to understand the mechanisms, principal uses, advantages, and limitations of each device.

Content‐based image retrieval system via sparse representation

The aim of image retrieval systems is to automatically assess, retrieve and represent relative images‐based user demand. However, the accuracy and speed of image retrieval are still an interesting topic of many researches. In this study, a new method based on sparse representation and iterative discrete wavelet transform has been proposed. To evaluate the applicability of the proposed feature‐based sparse representation for image retrieval technique, the precision at percent recall and average normalised modified retrieval rank are used as quantitative metrics to compare different methods. The experimental results show that the proposed method provides better performance in comparison with other methods.

Promising Large Scale Image Retrieval by Using Intelligent Semantic Binary Code Generation Technique

Abstract A scalable content based image retrieval system for large-scale www database is designed and implemented. Million images on internet is big challenge for accurate and efficient image retrieval as per user requirement. Proposed system exploits semantic binary code generation techniques with semantic hashing function, fine and coarse similarity measure technique, automatic and manual relevance feedback technique which improve accuracy, speed of image retrieval. With dramatic growth of internet technology, scalable image retrieval system is a need of recent web based image retrieval applications such as biomedical imaging, medical diagnosis, space science application etc. Proposed system accomplish requirement of scalable, accurate and swift image retrieval system. Experimental result clearly shows that performance of image retrieval is improved in term of accuracy, efficiency and retrieval time.

Massive Medical Images Retrieval System Based on Hadoop

In order to improve the efficiency of massive medical images retrieval, against the defects of the single-node medical image retrieval system, a massive medical images retrieval system based on Hadoop is put forward. Brushlet transform and Local binary patterns algorithm are introduced firstly to extract characteristics of the medical example image, and store the image feature library in the HDFS. Then using the Map to match the example image features with the features in the feature library, while the Reduce to receive the calculation results of each Map task and ranking the results according to the size of the similarity. At the end, find the optimal retrieval results of the medical images according to the ranking results. The experimental results show that compared with other medical image retrieval systems, the Hadoop based medical image retrieval system can reduce the time of image storage and retrieval, and improve the image retrieval speed.

Massive medical image retrieval system based on Hadoop

In order to improve the retrieval efficiency of massive medical images,a new medical image retrieval system was proposed based on distributed Hadoop to solve the low efficiency of medical image retrieval system based on single node.Firstly,the features of medical image were extracted by using Brushlet transform and Local Binary Pattern(LBP) algorithm,and the feature database was stored in the Hadoop Distributed File System(HDFS). Secondly,the Map was used to match the features of retrieval images and medical images in the library,and the matching results of the Map task were collected and sorted by the Reduce function. Finally,the optimum results of medical image retrieval were obtained according to the ordering. The test results show that,compared with other medical image retrieval systems,the proposed system reduces the time of image storage and retrieval,and improves the image retrieval speed.