Large scale approximate k-nearest neighbors search is an important and very useful technique for many multimedia retrieval applications. Most of existing search algorithms used the centralized indexing approaches and ...
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ISBN:
(纸本)9781479932610
Large scale approximate k-nearest neighbors search is an important and very useful technique for many multimedia retrieval applications. Most of existing search algorithms used the centralized indexing approaches and thus cannot meet the needs to search upon large scale datasets. This paper proposes an efficient and distributed approximate k-nearest neighbors search algorithm over a billion high-dimensional visual descriptors. We propose a randomized partitioning strategy and then design a two-layer distributed indexing scheme based on a neighborhood graph for large scale k-nearest neighbors search. The experimental results show that our method achieves excellent performance and scalability.
We propose to conduct a tutorial on importing, distributing, indexing, querying, and updating a large real-world trajectory dataset in the DBMS Secondo. Participants having installed the system and extracted the datas...
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ISBN:
(纸本)9781450369534
We propose to conduct a tutorial on importing, distributing, indexing, querying, and updating a large real-world trajectory dataset in the DBMS Secondo. Participants having installed the system and extracted the dataset will be able to follow the tutorial actively.
The creation of very large-scale multimedia search engines, with more than one billion images and videos, is a pressing need of digital societies where data is generated by multiple connected devices. Distributing sea...
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The creation of very large-scale multimedia search engines, with more than one billion images and videos, is a pressing need of digital societies where data is generated by multiple connected devices. Distributing search indexes in cloud environments is the inevitable solution to deal with the increasing scale of image and video collections. The distribution of such indexes in this setting raises multiple challenges such as the even partitioning of data space, load balancing across index nodes and the fusion of the results computed over multiple nodes. The main question behind this thesis is how to reduce and distribute the multimedia retrieval computational complexity? This thesis studies the extension of sparse hash inverted indexing to distributed settings. The main goal is to ensure that indexes are uniformly distributed across computing nodes while keeping similar documents on the same nodes. Load balancing is performed at both node and index level, to guarantee that the retrieval process is not delayed by nodes that have to inspect larger subsets of the index. Multimodal search requires the combination of the search results from individual modal- ities and document features. This thesis studies rank fusion techniques focused on reducing complexity by automatically selecting only the features that improve retrieval effectiveness. The achievements of this thesis span both distributed indexing and rank fusion research. Experiments across multiple datasets show that sparse hashes can be used to distribute documents and queries across index entries in a balanced and redundant manner across nodes. Rank fusion results show that is possible to reduce retrieval complexity and improve efficiency by searching only a subset of the feature indexes.
-Conventional IP/TCP designs encounter several safety and scalability concerns with the growing demand for application services. A novel Internet design, like a Content Center Network (CCN), was introduced to address ...
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-Conventional IP/TCP designs encounter several safety and scalability concerns with the growing demand for application services. A novel Internet design, like a Content Center Network (CCN), was introduced to address these issues comprehensively. Every hub within a CCN is responsible for data storage. The collaboration guarantees users quick data retrieval. By collaborating with dual caches, network peers can access data from their caches and leverage other peers' caches, resulting in improved cache utilization and overall network speed. The present study examines multimodal digital artworks' form, style, and action relationships and views them as holistic creative units. The study examines the complex structure of digital content following current information. We present a distributed index incorporating spatio-temporal information to address the challenges of storing and retrieving large amounts of spatio-temporal data. This distributed index combines internal R with external B+ trees to provide high concurrency and low latency indexing services for external applications. With double buffer technology and distributed index architecture, we can optimize the cache utility of content center networks and enhance the retrieval speed of multimedia data. Adopting the distributed index, designed to accommodate spatio-temporal data in the multimedia digital art design, can enhance large-scale storage and retrieval for Internet- future architectures.
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