Invited Talk by Prof. Jiri Matas: Image Retrieval in Large Collections - Beyond Similarity Search

Speaker: Prof. Jiri Matas Date: Thursday, January 08, 2015 Time: 14:15-15:15 Location: Seminar room MI 03.13.010, TUM Informatics Faculty, Garching

Abstract

Traditionally, image retrieval is formulated as the search for the most similar image or sub-image in the database. In large collections, such searches often produce results of limited interest, returning images with no new information and virtually identical content.

We propose retrieving the most dissimilar images of the object depicted in the query and develop two novel methods for a particular instances of "dissimilarity search": the zoom-in and zoom-in for finding the maximum and minimum spatial resolution depicting the query. Intuitively, the searches answer the "What is this?" and "Where is this?" questions. Furthermore, two problems straddling the boundary between image retrieval and data mining are formulated: for every pixel in the query image, (i) find the database image with the maximum resolution depicting the pixel and (ii) find the frequency with which it is photographed in detail.

An efficient and reliable solution is proposed based on two novel techniques, the hierarchical query expansion that exploits the document at a time (DAAT) inverted file and a geometric consistency verification sufficiently robust to prevent topic drift within a zooming search.

Experiments show that the proposed method finds surprisingly fine details on landmarks, even those that are hardly noticeable for humans.

Bio

Jiri Matas is a full professor at the Center for Machine Perception, Czech Technical University in Prague. He holds a PhD^? degree from the University of Surrey, UK (1995). He has published more than 200 papers in refereed journals and conferences. Google Scholar reports about 20 000 citations to his work and and an h-index above 50.

He received the best paper prize at the British Machine Vision Conferences in 2002 and 2005, at the Asian Conference on Computer Vision in 2007, at the Scandinavian Conference on Image Analysis and at the Image and Vision Computing New Zealand Conference in 2013. His students received a number of awards, e.g. Best Student paper at ICDAR 2013, Google Fellowship 2013, and various "Best Thesis" prizes.

J. Matas is on the editorial board of IJCV and was the Associate Editor-in-Chief of IEEE T. PAMI. He is a member of the ERC Computer Science and Informatics panel. He has served in various roles at major international conferences, e.g. ICCV, CVPR, ICPR, NIPS, ECCV, co-chairing ECCV 2004 and CVPR 2007. He is a program co-chair for ECCV 2016.

His research interests include object recognition, image retrieval, tracking, sequential pattern recognition, invariant feature detection, and Hough Transform and RANSAC-type optimization.

For more, see http://cmp.felk.cvut.cz/~matas.