Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A strong lower bound for approximate nearest neighbor searching
Information Processing Letters
AnnoSearch: Image Auto-Annotation by Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Nearest-neighbor-preserving embeddings
ACM Transactions on Algorithms (TALG)
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Learning reconfigurable hashing for diverse semantics
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
LDAHash: Improved Matching with Smaller Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Semi-supervised learning for scalable and robust visual search
Semi-supervised learning for scalable and robust visual search
Iterative quantization: A procrustean approach to learning binary codes
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Complementary hashing for approximate nearest neighbor search
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Semi-supervised spectral hashing for fast similarity search
Neurocomputing
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Image hashing based Approximate Nearest Neighbor (ANN) searching has drawn more and more attention in large-scale image dataset applications. It is still challenging to learn hashing codes to achieve good search performance. In this paper, we propose an image retrieval method based on boosting iterative quantization hashing method with query-adaptive reranking. Firstly, in boosting iterative quantization hashing embedding, we adopt boosting-based method to generate inputs to learn hashing functions. Then we optimize the hashing functions with a loss function by considering the relationship between samples. Once the hashing codes are generated, Query-Adaptive Reranking (QAR) method is proposed to learn bit-level weights for each category and query-adaptive weights for each hashing bit. In this way, the discrete Hamming distance value can be continuous, and many irrelevant returned images can be sorted to the back. We conduct experiments on three public datasets, and comparison results with six state-of-the-art methods to illustrate the effectiveness of the proposed method.