Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A statistical framework for genomic data fusion
Bioinformatics
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Introduction to Information Retrieval
Introduction to Information Retrieval
Multimedia ontology learning for automatic annotation and video browsing
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Nearest Neighbor Retrieval Using Distance-Based Hashing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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The paper presents a novel framework for learning the hash functions for indexing through Multiple Kernel Learning. The Distance Based Hashing function is applied which does the object projection to hash space by preserving inter object distances. In recent works, the kernel matrix has been proved to be more accurate representation of similarity in various recognition problems. Our framework learns the optimal kernel for hashing by parametrized linear combination of base kernels. A novel application of Genetic Algorithm for the optimization of kernel combination parameters is presented. We also define new texture based feature representation for images. Our proposed framework can also be applied for optimal combination of multiple sources for indexing. The evaluation of the proposed framework is presented for CIFAR-10 dataset by applying individual and combination of different features. Additionally, the primary experimental results with MNIST dataset is also presented.