Accurate and efficient cross-domain visual matching leveraging multiple feature representations

Cross-domain visual matching aims at finding visually similar images across a wide range of visual domains, and has shown a practical impact on a number of applications. Unfortunately, the state-of-the-art approach, which estimates the relative importance of the single feature dimensions still suffers from low matching accuracy and high time cost. To this end, this paper proposes a novel cross-domain visual matching framework leveraging multiple feature representations. To integrate the discriminative power of multiple features, we develop a data-driven, query specific feature fusion model, which estimates the relative importance of the individual feature dimensions as well as the weight vector among multiple features simultaneously. Moreover, to alleviate the computational burden of an exhaustive subimage search, we design a speedup scheme, which employs hyperplane hashing for rapidly collecting the hard-negatives. Extensive experiments carried out on various matching tasks demonstrate that the proposed approach outperforms the state-of-the-art in both accuracy and efficiency. © 2013 Springer-Verlag Berlin Heidelberg.Cross-domain visual matching aims at finding visually similar images across a wide range of visual domains, and has shown a practical impact on a number of applications. Unfortunately, the state-of-the-art approach, which estimates the relative importance of the single feature dimensions still suffers from low matching accuracy and high time cost. To this end, this paper proposes a novel cross-domain visual matching framework leveraging multiple feature representations. To integrate the discriminative power of multiple features, we develop a data-driven, query specific feature fusion model, which estimates the relative importance of the individual feature dimensions as well as the weight vector among multiple features simultaneously. Moreover, to alleviate the computational burden of an exhaustive subimage search, we design a speedup scheme, which employs hyperplane hashing for rapidly collecting the hard-negatives. Extensive experiments carried out on various matching tasks demonstrate that the proposed approach outperforms the state-of-the-art in both accuracy and efficiency. © 2013 Springer-Verlag Berlin Heidelberg

ChemInform Abstract: Local Probe Oxidation of Self-Assembled Monolayers: Templates for the Assembly of Functional Nanostructures.

The local oxidation of self-assembled monolayers with a scanning probe is a promising method for the generation of structures with chemical functionalities on the nanometer scale. This technique, which takes advantage of the chemical stability and versatility of selfassembled monolayers and the ability to pattern these monolayers by scanning-probe-based oxidation methods, enables the hierarchical assembly of complex structures in a controlled manner. Surface modification can be followed by the assembly of a further functional monolayer and/or additional surface-modification reactions in the targeted, sequential construction of functional device features