Now showing items 11-16 of 16
Efficient Representation of Local Geometry for Large Scale Object Retrieval
State of the art methods for image and object retrieval exploit both appearance (via visual words) and local geometry (spatial extent, relative pose). In large scale problems, memory becomes a limiting factor - local ...
Construction of Precise Local Affine Frames
We propose a novel method for the refinement of Maximally Stable Extremal Region (MSER) boundaries to sub-pixel precision by taking into account the intensity function in the 2 × 2 neighborhood of the contour points. The ...
Image Matching and Retrieval by Repetitive Patterns
Detection of repetitive patterns in images has been studied for a long time in computer vision. This paper discusses a method for representing a lattice or line pattern by shift-invariant descriptor of the repeating element. ...
Geometric min-Hashing: Finding a (Thick) Needle in a Haystack
We propose a novel hashing scheme for image retrieval, clustering and automatic object discovery. Unlike commonly used bag-of-words approaches, the spatial extent of image features is exploited in our method. The geometric ...
Large-Scale Discovery of Spatially Related Images
We propose a randomized data mining method that finds clusters of spatially overlapping images. The core of the method relies on the min-Hash algorithm for fast detection of pairs of images with spatial overlap, the so-called ...
Unsupervised Discovery of Co-occurrence in Sparse High Dimensional Data
An efficient min-Hash based algorithm for discovery of dependencies in sparse high-dimensional data is presented. The dependencies are represented by sets of features co-occurring with high probability and are called ...