A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Object fingerprints for content analysis with applications to street landmark localization
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Logo retrieval with a contrario visual query expansion
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Point-context descriptor based region search for logo recognition
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Efficient clothing retrieval with semantic-preserving visual phrases
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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Towards building a practical large-scale logo retrieval system, we propose a novel approach to extract and combine local features for effective logo retrieval. Instead of global feature extraction by modeling the web logo as a whole, we extract the local feature phrases to form a visual codebook and build an inverted file storing the features to accelerate the indexing process. Then we divide logos into several groups according to local feature type based on which feature can model the logo best and naming as "Point-type", "Shape-type" and "Patch-type". We develop a strategy of adaptive feature selection by a weight updating mechanism. To evaluate the performance, we have built a new challenging dataset which consists of 60 international corporations' logos. Experiments and comparisons demonstrate the superior performance to previous retrieval algorithms.