We analyze the nature of the relevance feedback problem in a continuous representation
space in the context of content-based image retrieval. Emphasis is put on exploring the …

Most current content-based image retrieval systems are still incapable of providing users
with their desired results. The major difficulty lies in the gap between low-level image …

Novel database applications, such as multimedia, data mining, e-commerce, and many
others, make intensive use of similarity queries in order to retrieve the objects that better fit a …

The learning-enhanced relevance feedback has been one of the most active research areas
in content-based image retrieval in recent years. However, few methods using the relevance …

We review the major paradigms for approximate similarity queries and propose a
classification schema that easily allows existing approaches to be compared along several …

The difficulty and cost of providing rich and reliable textual annotations for images in large
databases, as well as the “linguistic gap” associated to these annotations, explains why the …

When a large dataset is given, it is not desirable for a user to read all tuples one-by-one in
the whole dataset to find satisfied tuples. The traditional top-k query finds the best k tuples …

Target search in content-based image retrieval (CBIR) systems refers to finding a specific
(target) image such as a particular registered logo or a specific historical photograph …

(57) ABSTRACT A computer system presents retrieved documents to a user, With
documents most similar to documents in Which the user previously showed high interest …

With the emergence of applications that require contentbased similarity retrieval, techniques
to support such a retrieval paradigm over database systems have emerged as a critical area …