Inherent Fusion: Towards Scalable Multi-Modal Similarity Search
| Authors | |
|---|---|
| Year of publication | 2016 |
| Type | Article in Periodical |
| Magazine / Source | Journal of Database Management |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.4018/JDM.2016100101 |
| Field | Informatics |
| Keywords | Content-Based Retrieval; Evaluation; Image Retrieval; Late Fusion; Multi-Modal Search; Scalability; Similarity Searching |
| Description | The rapid growth of unstructured data, commonly denoted as the Big Data challenge, requires new technologies that are capable of dealing with complex data objects such as multimedia. In this work, the authors focus on the content-based retrieval approach, which is able to organize such data by exploiting the similarity of data content. In particular, they focus on solutions that are able to combine multiple similarity measures during the query evaluation. The authors introduce a classification of existing approaches and analyze their performance in terms of effectiveness, efficiency, and scalability. Further, they present a novel technique of inherent fusion that combines the efficiency of fast indexed retrieval with the effectiveness of ranking methods. The performance of all discussed methods is evaluated by extensive experiments with user participation. |
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