Deep Learning Perspectives on Efficient Image Matching in Natural Image Databases

With the proliferation of digital content, efficient image matching in natural image databases has become paramount. Traditional image matching techniques, while effective to a certain extent, face challenges in dealing with the high variability inherent in natural images. This research delves into the application of deep learning models, particularly Convolutional Neural Networks (CNNs), Siamese Networks, and Triplet Networks, to address these challenges. We introduce various techniques to enhance efficiency, such as data augmentation, transfer learning, dimensionality reduction, efficient sampling, and the amalgamation of traditional computer vision strategies with deep learning. Our experimental results, garnered from specific dataset, demonstrate significant improvements in image matching efficiency, as quantified by metrics like precision, recall, F1-Score, and matching time. The findings underscore the potential of deep learning as a transformative tool for natural image database matching, setting the stage for further research and optimization in this domain

An Extreme Learning Machine-Relevance Feedback Framework for Enhancing the Accuracy of a Hybrid Image Retrieval System

The process of searching, indexing and retrieving images from a massive database is a challenging task and the solution to these problems is an efficient image retrieval system. In this paper, a unique hybrid Content-based image retrieval system is proposed where different attributes of an image like texture, color and shape are extracted by using Gray level co-occurrence matrix (GLCM), color moment and various region props procedure respectively. A hybrid feature matrix or vector (HFV) is formed by an integration of feature vectors belonging to three individual visual attributes. This HFV is given as an input to an Extreme learning machine (ELM) classifier which is based on a solitary hidden layer of neurons and also is a type of feed-forward neural system. ELM performs efficient class prediction of the query image based on the pre-trained data. Lastly, to capture the high level human semantic information, Relevance feedback (RF) is utilized to retrain or reformulate the training of ELM. The advantage of the proposed system is that a combination of an ELM-RF framework leads to an evolution of a modified learning and intelligent classification system. To measure the efficiency of the proposed system, various parameters like Precision, Recall and Accuracy are evaluated. Average precision of 93.05%, 81.03%, 75.8% and 90.14% is obtained respectively on Corel-1K, Corel-5K, Corel-10K and GHIM-10 benchmark datasets. The experimental analysis portrays that the implemented technique outmatches many state-of-the-art related approaches depicting varied hybrid CBIR system

Arquitectura de microservicios para extracción de características en sistemas de recuperación de imágenes basada en contenido

Introduction: Content-based image retrieval systems allow users, using a reference image, to retrieve those similar to their query. In the conception of such systems for the Web, aspects related to the high volume of existing digital images must be considered, which generate problems during their processing in real time, specifically in the extraction of their visual features, the object of this investigation. Objective: Contribute to the mitigation of scalability, elasticity, availability and reliability problems presented by the module for extracting its visual characteristics from a content-based image retrieval system. Methodology: The definition, design and implementation of a proposal for architecture based on microservices was carried out, followed by the execution of tests using simulation-based experiments for the evaluation of said proposal, presenting the respective analysis and discussion of the results provided by the indicator panel of the Google Cloud console. Results: A microservices-based architecture where each algorithm / technique for extracting features from a digital image was implemented as a microservice under the Google Cloud infrastructure. Conclusions: This architectural proposal supported by microservices favors its automatic scalability during the extraction of features from large volumes of images and can be used in the design and construction of other modules of a content-based image retrieval system.Introducción: Los sistemas de recuperación de imágenes basada en contenido permiten a los usuarios, por medio de una imagen de referencia, recuperar aquellas similares a su consulta.  En la concepción de dichos sistemas para la Web, deben ser considerados aspectos relacionados al alto volumen de imágenes digitales existentes, que generan problemas durante su procesamiento en tiempo real, específicamente en la extracción de sus características visuales, objeto de esta investigación. Objetivo: Contribuir en la mitigación de los problemas de escalabilidad, elasticidad, disponibilidad y confiabilidad presentada por el módulo de extracción de sus características visuales de un sistema de recuperación de imágenes basada en contenido. Metodología: Se realizó la definición, diseño e implementación de una propuesta de arquitectura basada en microservicios y posteriormente la ejecución de pruebas mediante experimentos basados en simulación para la evaluación de dicha propuesta, presentando el respectivo análisis y discusión de los resultados entregados por el tablero de indicadores de la consola de Google Cloud.   Resultados: Una arquitectura basada en microservicios donde cada algoritmo/técnica de extracción de características de una imagen digital fue implementada como un microservicio bajo la infraestructura de Google Cloud. Conclusiones: Esta propuesta arquitectural soportada en microservicios favorece su escalabilidad automática durante la extracción de características de grandes volúmenes de imágenes y puede ser usada en el diseño y construcción de otros módulos de un sistema de recuperación de imágenes basada en contenid

Microservices architecture for feature extraction in content-based image retrieval systems

Introducción: Los sistemas de recuperación de imágenes basada en contenido permiten a los usuarios, por medio de una imagen de referencia, recuperar aquellas similares a su consulta.  En la concepción de dichos sistemas para la Web, deben ser considerados aspectos relacionados al alto volumen de imágenes digitales existentes, que generan problemas durante su procesamiento en tiempo real, específicamente en la extracción de sus características visuales, objeto de esta investigación. Objetivo: Contribuir en la mitigación de los problemas de escalabilidad, elasticidad, disponibilidad y confiabilidad presentada por el módulo de extracción de sus características visuales de un sistema de recuperación de imágenes basada en contenido. Metodología: Se realizó la definición, diseño e implementación de una propuesta de arquitectura basada en microservicios y posteriormente la ejecución de pruebas mediante experimentos basados en simulación para la evaluación de dicha propuesta, presentando el respectivo análisis y discusión de los resultados entregados por el tablero de indicadores de la consola de Google Cloud.   Resultados: Una arquitectura basada en microservicios donde cada algoritmo/técnica de extracción de características de una imagen digital fue implementada como un microservicio bajo la infraestructura de Google Cloud. Conclusiones: Esta propuesta arquitectural soportada en microservicios favorece su escalabilidad automática durante la extracción de características de grandes volúmenes de imágenes y puede ser usada en el diseño y construcción de otros módulos de un sistema de recuperación de imágenes basada en contenidoIntroduction: Content-based image retrieval systems allow users, using a reference image, to retrieve those similar to their query. In the conception of such systems for the Web, aspects related to the high volume of existing digital images must be considered, which generate problems during their processing in real time, specifically in the extraction of their visual features, the object of this investigation. Objective: Contribute to the mitigation of scalability, elasticity, availability and reliability problems presented by the module for extracting its visual characteristics from a content-based image retrieval system. Methodology: The definition, design and implementation of a proposal for architecture based on microservices was carried out, followed by the execution of tests using simulation-based experiments for the evaluation of said proposal, presenting the respective analysis and discussion of the results provided by the indicator panel of the Google Cloud console. Results: A microservices-based architecture where each algorithm / technique for extracting features from a digital image was implemented as a microservice under the Google Cloud infrastructure. Conclusions: This architectural proposal supported by microservices favors its automatic scalability during the extraction of features from large volumes of images and can be used in the design and construction of other modules of a content-based image retrieval system