Informace o publikaci

Inherent Fusion: Towards Scalable Multi-Modal Similarity Search

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BUDÍKOVÁ Petra BATKO Michal NOVÁK David ZEZULA Pavel

Rok publikování 2016
Druh Článek v odborném periodiku
Časopis / Zdroj Journal of Database Management
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
Doi http://dx.doi.org/10.4018/JDM.2016100101
Obor Informatika
Klíčová slova Content-Based Retrieval; Evaluation; Image Retrieval; Late Fusion; Multi-Modal Search; Scalability; Similarity Searching
Popis 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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