Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of hash-based text retrieval
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
International Journal of Approximate Reasoning
Fast Similarity Search for Learned Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-taught hashing for fast similarity search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Composite hashing with multiple information sources
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Supervised hashing with kernels
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Semi-Supervised Hashing for Large-Scale Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Approximate nearest neighbor search within large scale image datasets strongly demands efficient and effective algorithms. One promising strategy is to compute compact bits string via the hashing scheme as representation of data examples, which can dramatically reduce query time and storage requirements. In this paper, we propose a novel Cauchy graph-based hashing algorithm for the first time, which can capture more local topology semantics than Laplacian embedding. In particular, greater similarities are achieved through Cauchy embedding mapped from the pairs of smaller distance over the original data space. Then regularized kernel least-squares, with its closed form solution, is applied to efficiently learn hash functions. The experimental evaluations over several noted image retrieval benchmarks, MNIST, CIFAR-10 and USPS, demonstrate that performance of the proposed hashing algorithm is quite comparable with the state-of-the-art hashing techniques in searching semantic similar neighbors, especially in quite short length hash codes, such as those of only 4, 6, and 8 bits.