查看文章

A massive and ever growing amount of data collections, including visual and multimedia content are available today. Such content usually possesses additional information, as text or other metadata, to form a rather sparse and noisy, yet rich and diverse source of annotation. Although the text-based retrieval models are well established, they ignore the rich source of information encoded in the visual data. In contrast, the promising content-based retrieval technologies, capable of considering the multimedia content, still face obstacles for mapping the low level features into high level semantic concepts. Supervised approaches based on relevance feedback techniques have been employed for mitigating such gap on visual retrieval tasks. Although often quite effective, such methods rely only on labeled data, which can severely impact the retrieval effectiveness when the number of user interventions is insufficient. In …