Geometric Hashing: An Overview
IEEE Computational Science & Engineering
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Scalable near identical image and shot detection
Proceedings of the 6th ACM international conference on Image and video retrieval
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Embedding spatial information into image content description for scene retrieval
Pattern Recognition
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature map hashing: sub-linear indexing of appearance and global geometry
Proceedings of the international conference on Multimedia
Building contextual visual vocabulary for large-scale image applications
Proceedings of the international conference on Multimedia
Partition min-hash for partial duplicate image discovery
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Scalable logo recognition in real-world images
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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Local features are omnipresent in computer vision applications and an important building block for applications like object recognition and image retrieval. Such applications often involve feature matching or use an inverted index to efficiently retrieve similar local features for a given query. In such cases it is commonly known that local features are often not informative yielding mismatches and false positives when used for feature matching (see Figure 1)or retrieval. As a result retrieval systems employ expensive post-retrieval verification steps. These observations underline the importance of embedding spatial information of a local image region directly within the index to decrease the number of false positives upon retrieval. We present our latest method for embedding spatial information into an index by bundling local feature triples for logo recognition. We give an overview of our feature representation, describe currently ongoing research and pose open questions for discussion.