Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Large scale image copy detection evaluation
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Bag-of-visual-words vs global image descriptors on two-stage multimodal retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Content based image retrieval using visual-words distribution entropy
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Enriching and localizing semantic tags in internet videos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
AIEMPro '11 Proceedings of the 2011 ACM international workshop on Automated media analysis and production for novel TV services
Dynamic two-stage image retrieval from large multimedia databases
Information Processing and Management: an International Journal
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TOP-SURF is an image descriptor that combines interest points with visual words, resulting in a high performance yet compact descriptor that is designed with a wide range of content-based image retrieval applications in mind. TOP-SURF offers the flexibility to vary descriptor size and supports very fast image matching. In addition to the source code for the visual word extraction and comparisons, we also provide a high level API and very large pre-computed codebooks targeting web image content for both research and teaching purposes.