Multi-modal Similarity Retrieval with a Shared Distributed Data Store
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Proceedings |
| Conference | Scalable Information Systems: 5th International Conference, INFOSCALE 2014, Seoul, South Korea, September 25-26, 2014, Revised Selected Papers |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1007/978-3-319-16868-5_3 |
| Field | Informatics |
| Keywords | similarity search; multi-modal search; Big Data; scalability |
| Description | We propose a generic system architecture for large-scale similarity search in various types of digital data. The architecture combines contemporary highly-scalable distributed data stores with recent efficient similarity indexes and also with other types of search indexes. The system is designed to provide several types of queries – distance-based similarity queries, term-based queries, attribute queries, and advanced queries combining several search aspects (modalities). The first part of this work is devoted to the generic architecture and to description of a similarity index PPP-Codes that is suitable for our system. In the second part, we describe a specific instance of this architecture that manages a 106 million image collection providing content-based visual search, keyword search, attribute-based access, and their combinations. |
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