Fast visual vocabulary construction for image retrieval using skewed-split k-d trees

Gialampoukidis, Ilias; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:http://dx.doi.org/10.1007/978-3-319-27671-7_39

Fast visual vocabulary construction for image retrieval using skewed-split k-d trees

Citation

Gialampoukidis I, Vrochidis S, Kompatsiaris I. Fast visual vocabulary construction for image retrieval using skewed-split k-d trees. In: Tian Q, Sebe N, Qi GJ, Huet B, Hong R, Liu X, editors. 22nd International Conference on MultiMedia Modeling (MMM16); 2016 gen.4-6; Miami (FL, USA). [place unknown]: Springer; 2016. Part 1, Poster papers; p. 466-477. DOI: 10.1007/978-3-319-27671-7_39

Abstract

Most of the image retrieval approaches nowadays are based on the Bag-of-Words (BoW) model, which allows for representing an image efficiently and quickly. The efficiency of the BoW model is related to the efficiency of the visual vocabulary. In general, visual vocabularies are created by clustering all available visual features, formulating specific patterns. Clustering techniques are k-means oriented and they are replaced by approximate k-means methods for very large datasets. In this work, we propose a faster construction of visual vocabularies compared to the existing method in the case of SIFT descriptors, based on our observation that the values of the 128-dimensional SIFT descriptors follow the exponential distribution. The application of our method to image retrieval in specific image datasets showed that the mean Average Precision is not reduced by our approximation, despite that the visual vocabulary has been constructed significantly faster compared to the state of the art methods.