This paper presents two content-based image retrieval frameworks with relevance feedback
based on genetic programming. The first framework exploits only the user indication of …

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 …

This paper presents an interactive technique for remote sensing image classification. In our
proposal, users are able to interact with the classification system, indicating regions of …

This paper deals with the problem of image retrieval from large image databases. A
particularly interesting problem is the retrieval of all images which are similar to one in the …

This paper presents a new relevance feedback method for content-based image retrieval
using local image features. This method adopts a genetic programming approach to learn …

The interaction of users with search services has been recognized as an important
mechanism for expressing and handling user information needs. One traditional approach …

Large amounts of image data have been produced on the Internet over the past several
years. As a kind of effective retrieval way, the content-based image retrieval (CBIR) has …

Content-based image retrieval (CBIR) has been an active topic of research in computer
vision for decades. Due to the existence of semantic gap, much research has also been …

Relevance feedback is an effective approach to improve the performance of image retrieval
by leveraging the labeling of human. In order to alleviate the burden of labeling, active …

In this chapter, the dual estimation and reduced order modeling of a damaging structure is
studied. In this regard, proper orthogonal decomposition is considered for reduced order …