Jianlong Fu
ABSTRACT
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.
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Digital Library
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Index Terms
- Effective logo retrieval with adaptive local feature selection
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