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Despite the fact that Second Order Similarity (SOS) has been used with significant success in tasks such as graph matching and clustering, it has not been exploited for learning local descriptors. In this work, we explore the potential of SOS in the field of descriptor learning by building upon the intuition that a positive pair of matching points should exhibit similar distances with respect to other points in the embedding space. Thus, we propose a novel regularization term, named Second Order Similarity Regularization (SOSR), that follows this principle. By incorporating SOSR into training, our learned descriptor achieves state-of-the-art performance on several challenging benchmarks containing distinct tasks ranging from local patch retrieval to structure from motion. Furthermore, by designing a von Mises-Fischer distribution based evaluation method, we link the utilization of the descriptor space to the matching performance, thus demonstrating the effectiveness of our proposed SOSR. Extensive experimental results, empirical evidence, and in-depth analysis are provided, indicating that SOSR can significantly boost the matching performance of the learned descriptor.

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Super-hydrophobic surfaces are attractive due to self-cleaning and anti-corrosive behaviors in harsh environments. Laser texturing offers a facile method to produce super-hydrophobic surfaces. However, the results indicated that the fresh laser ablated surface was generally super-hydrophilic and then gradually reached super-hydrophobic state when exposed to ambient air for certain time. Investigating wettability changing mechanism could contribute to reducing wettability transition period and improving industrial productivity. To solve this problem, we have studied the bare aluminum surface, fresh laser ablated super-hydrophilic surface, 15-day air exposed surface, and the aged super-hydrophobic surface by time-dependent water contact angle (WCA) and rolling angle (RA), scanning electron microscopy (SEM), 3D profile and X-ray photoelectron spectroscopy (XPS). The origins of super-hydrophilicity of the fresh laser ablated surface are identified as (1) the formation of hierarchical rough structures and (2) the surface chemical modifications (the decrease of nonpolar carbon, the formation of hydrophilic alumina and residual unsaturated atoms). The chemisorbed nonpolar airborne hydrocarbons from air moisture contributed to the gradual super-hydrophobic transition, which can be proved by the thermal annealing experiment. Particularly, to clearly explore the wettability transition mechanism, we extensively discussed why the laser-induced freshly outer layer was super-hydrophilic and how the airborne hydrocarbons were chemisorbed. This work not only provides useful insights into the formation mechanism of laser ablated super-hydrophobic surfaces, but also further guides industry to effectively modify surface chemistry to reduce wettability transition period and rapidly produce stable and durable super-hydrophobic surfaces.

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