| dc.description.abstract |
With the increase in popularity of image-based applications, users are retrieving images using more sophisticated and complex queries. We present three types of complex queries, namely, long, ambiguous, and abstract. Each type of query has its own characteristics/complexities and thus leads to imprecise and incomplete image retrieval. Existing methods for image retrieval are unable to deal with the high complexity of such queries. Search engines need to integrate their image retrieval process with knowledge to obtain rich semantics for effective retrieval. We propose a framework, Image Retrieval using Knowledge Embedding (ImReKE), for embedding knowledge with images and queries, allowing retrieval approaches to understand the context of queries and images in a better way. ImReKE (IR_Approach, Knowledge_Base) takes two inputs, namely, an image retrieval approach and a knowledge base. It selects quality concepts (concepts that possess properties such as rarity, newness, etc.) from the knowledge base to provide rich semantic representations for queries and images to be leveraged by the image retrieval approach. For the first time, an effective knowledge base that exploits both the visual and textual information of concepts has been developed. Our extensive experiments demonstrate that the proposed framework improves image retrieval significantly for all types of complex queries. The improvement is remarkable in the case of abstract queries, which have not yet been dealt with explicitly in the existing literature. We also compare the quality of our knowledge base with the existing text-based knowledge bases, such as ConceptNet, ImageNet, and the like. |
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