Recovering Images Based On Their Contents In A Distributed System

The use of peer-to-peer networking as an alternative has become more common in recent years as a means of facilitating the scalable movement of multimedia data. The process of carrying out content-based retrieval in peer-to-peer networks, which are characterized by the distribution of huge quantities of visual data across several nodes, is an important yet challenging topic. In this study, we offer a scalable strategy for content-based picture retrieval in peer-to-peer networks utilizing the bag-of-visual-words paradigm. This is in contrast to most of the previous approaches, which were focused on indexing high-dimensional visual characteristics and had limitations on their scalability. When images are scattered over the whole of the peer-to-peer network, the key challenge lies in efficiently getting a global codebook. This is not a problem in centralized setups because it is easier to access the codebook. A static codebook is less helpful for retrieval tasks in a peer-to-peer network because of the dynamic nature of the growth of the network itself. In order to accomplish this, we present a method for dynamically updating the codebook. This method works by distributing the workload evenly across the nodes that are responsible for handling different code words and optimizing the mutual information that exists between the generated codebook and the relevance information. In order to speed up the retrieval process and cut down on network overhead, researchers are investigating several methods for index trimming. The comprehensive experimental data that we have collected indicates that the method that has been recommended is scalable in dynamic and scattered peer-to-peer networks, all while improving retrieval accuracy

Image Reordering Based On The Theme Of Diversity

A unique thematic function is proposed to improve the code within the P2P environment, which both displays relevant information as well as balances workload. Therefore, we recommend that you use the updated codebook methodology to improve the information you replace with the resulting codebook and related information, as well as the workload balance between nodes that manage different coding words. An updated version of the Codebook is proposed to be distributed according to the distribution / mixing of personal passwords, which improves target performance at a lower update cost. While most current approaches focus on restricting scalability as well as optimizing high-dimensional visual features, we include content-based images in peer-to-peer systems in this paper to peer-to-peer word models. We recommend using the case. This season's codebook should be updated periodically, instead of the regular time. Within this paper, we offer a unique presenting method to increase mobility around the world, demonstrating both balance and workload. In addition, the peer-to-peer network is dynamically developed, making a stable codebook less efficient for retrieval operations. In order to be able to improve recovery performance and reduce network costs, indexing trimming techniques have been developed. Unlike the central environment, the key challenge is to efficiently acquire an efficient global code book, as images are distributed across peer-to-peer networks

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

A Location-Aware Middleware Framework for Collaborative Visual Information Discovery and Retrieval

This work addresses the problem of scalable location-aware distributed indexing to enable the leveraging of collaborative effort for the construction and maintenance of world-scale visual maps and models which could support numerous activities including navigation, visual localization, persistent surveillance, structure from motion, and hazard or disaster detection. Current distributed approaches to mapping and modeling fail to incorporate global geospatial addressing and are limited in their functionality to customize search. Our solution is a peer-to-peer middleware framework based on XOR distance routing which employs a Hilbert Space curve addressing scheme in a novel distributed geographic index. This allows for a universal addressing scheme supporting publish and search in dynamic environments while ensuring global availability of the model and scalability with respect to geographic size and number of users. The framework is evaluated using large-scale network simulations and a search application that supports visual navigation in real-world experiments

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Accessible Method for Content-Based Image Retrieval In Peer To- Peer Networks

I propose a scalable approach for content-based image retrieval in shared systems by utilizing the sack of-visual words show. Contrasted and brought together conditions, the key test is to proficiently acquire a worldwide codebook, as pictures are conveyed over the entire distributed system. Furthermore, a distributed system frequently develops progressively, which makes a static codebook less successful for recovery assignments. Along these lines, we propose a dynamic codebook refreshing technique by upgrading the common data between the resultant codebook and significance data, and the workload adjust among nodes that oversee distinctive codewords