Modern Information Retrieval
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Constructing visual phrases for effective and efficient object-based image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Efficient Visual Search of Videos Cast as Text Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing local configurations of features for scalable content-based video copy detection
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
A comparative evaluation of interest point detectors and local descriptors for visual SLAM
Machine Vision and Applications
Retrieving landmark and non-landmark images from community photo collections
Proceedings of the international conference on Multimedia
Feature map hashing: sub-linear indexing of appearance and global geometry
Proceedings of the international conference on Multimedia
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The success of image object retrieval systems relies on the visual bag-of-words paradigm, which allows image retrieval systems to adopt a retrieval strategy analogous to text retrieval. In this paper we propose two spatially-aware retrieval strategies for image object retrieval that replaces the vector space model. The advantage of the proposed spatially-aware indexing and retrieval strategies are threefold: (1) It allows for the deployment of small visual vocabularies, (2) the number of images evaluated at retrieval time is significantly reduced, and (3) it eliminates the need for a post-retrieval phase, which is normally used to test the spatial composition of the visual words in the retrieved images. The first spatially-aware retrieval strategy explores the direct neighbourhood of two local features for common visual words to determine the similarity of the region surrounding the local features. The second strategy embeds the spatial composition of its neighbourhood directly in the index using edge signatures. Both strategies rely on the coherence of the neighbourhood of points in different images containing similar objects. The comparison of the spatially-aware retrieval strategies against the vector space baseline shows a significant improvement in terms of early precision, and at the same time significantly reduce the number of candidates to be considered at retrieval time.