Density-based selection of local features [MIR05]

Keywords: image retrieval, local features, discriminant, density estimation
This work started in collaboration with the NII (National Institute of Japan) within the scope of my visit in Tokyo (july 2005). Local features are well-suited to content-based image retrieval because of their locality, their local uniqueness and their high information content [4]. However, as they are selected only according to the local information content in the image, there is no guaranty that they will be distinctive in a large set of images. A local feature corresponding to a high saliency in the image can be highly redundant in some specific databases, such as the TV news database stored at NII in which textual characters are extremely frequent. To overcome this issue, we propose [5] to select relevant local features directly according to their discrimination power in a specific set of images. By computing the density of the local features in a source database with a new fast non parametric density estimation technique, it is indeed possible to select quickly the most rare local features in a large set of images. Figure illustrates the difference between the 20 most salient points of an image and the 20 most rare points according to their density in a large image database. Currently, we are also looking at selecting local features according to their density in a single image or in a class of images, as done for textual features with TF/IDF techniques.

left: 20 most salient points - right: 20 most rare points

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[2] "Distinctive image features from scale-invariant keypoints", D. Lowe, Int. Journal of Computer Vision, vol. 60, no. 2, pp. 91--110, 2004.
[3] "Content-based video copy detection in large databases: A local fingerprints statistical similarity search approach", A. Joly, C. Frélicot and O. Buisson, in Proceedings of the Int. Conf. on Image Processing, 2005.
[4] K. Mikolajczyk, C. Schmid. "A performance evaluation of local descriptors," cvpr, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. 1615--1630, 2005.
[5] "Discriminant Local Features Selection using Efficient Density Estimation in a Large Database", A. Joly and O. Buisson, ACM Int. Workshop on Multimedia Information Retrieval, invited paper, 2005.