Interactive remote-sensing image retrieval using active relevance feedback

Ferecatu, Marin

doi:10.1109/TGRS.2007.892007

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Interactive remote-sensing image retrieval using active relevance feedback

2007-04-01

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Ferecatu, Marin *

Boujemaa, Nozha

DOI：10.1109/TGRS.2007.892007

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Abstract

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As the resolution of remote-sensing imagery increases, the full complexity of the scenes becomes increasingly difficult to approach. User-defined classes in large image databases are often composed of several groups of images and span very different scales in the space of low-level visual descriptors. The interactive retrieval of such image classes is then very difficult. To address this challenge, we evaluate here, in the context of satellite image retrieval, two general improvements for relevance feedback using support vector machines (SVMs). First, to optimize the transfer of information between the user and the system, we focus on the criterion employed by the system for selecting the images presented to the user at every feedback round. We put forward an active-learning selection criterion that minimizes redundancy between the candidate images shown to the user. Second, for image classes spanning very different scales in the low-level description space, we find that a high sensitivity of the SVM to the scale of the data brings about a low retrieval performance. We argue that the insensitivity to scale is desirable in this context, and we show how to obtain it by the use of specific kernel functions. Experimental evaluation of both ranking and classification performance on a ground-truth database of satellite images confirms the effectiveness of our approach.

Keywords:

active learning

image retrieval

kernel function

reduction of redundancy

sample selection

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