A contextual dissimilarity measure for accurate and efficient image search

In this paper we present two contributions to improve accuracy and speed of an image search system based on bag-of-features: a contextual dissimilarity measure (CDM) and an efficient search structure for visual word vectors. Our measure (CDM) takes into account the local distribution of the vectors and iteratively estimates distance correcting terms. These terms are subsequently used to update an existing distance, thereby modifying the neighborhood structure. Experimental results on the Nist?er-Stew?enius dataset show that our approach significantly outperforms the state-of-the-art in terms of accuracy. Our efficient search structure for visual word vectors is a two-level scheme using inverted files. The first level partitions the image set into clusters of images. At query time, only a subset of clusters of the second level has to be searched. This method allows fast querying in large sets of images. We evaluate the gain in speed and the loss in accuracy on large datasets (up to 1 m...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Herve Jegou, Hedi Harzallah, Cordelia Schmid
• Keywords: Computer Vision, Contextual Dissimilarity Measure, CVPR 2007, Distance Correcting Terms, Efficient Search Structure, Neighborhood Structure, Visual Word Vectors