Abstract
In this paper, we introduce the general architecture of an image-search engine based on pre-attentive similarities. Local features are computed in key points to represent local properties of the images. The location of key points, where local features are computed, is discussed. We present two new key point detectors designed for image retrieval, both based on multi-resolution: the contrast-based point detector, and the wavelet-based point detector. Four different local features are used in our system: differential invariants, texture, shape and colour. The local information computed in each key point is stored in 2D histograms to allow fast querying. We study the choice of the key points detector depending on the feature used, for different test sets. The Harris corner detector is used for benchmarking. Uniformly distributed points are also used, and we conclude for which applications they are effective. Finally, we show that point detector and feature efficiency depend upon the test set studied.
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