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Color information has been acknowledged for its important role in object recognition and scene classification. How to describe the color characteristics and extract combined spatial and chromatic feature is a challenging task in computer vision. In this paper we extend the robust SIFT feature on processed opponent color channels to obtain a spatio-...
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Citations
... As stated in [35], the purpose of dimension reduction is to seek a transformation, which could convert the original data in high-dimensional space into a low-dimensional space while preserving and enhancing the data intrinsic structure. With this, redundant information in high-dimensional data is removed to explicitly represent intrinsic structure in low-dimensional space. ...
In our increasingly “data-abundant” society, remote sensing big data perform massive, high dimension and heterogeneity features, which could result in “dimension disaster” to various extent. It is worth mentioning that the past two decades have witnessed a number of dimensional reductions to weak the spatiotemporal redundancy and simplify the calculation in remote sensing information extraction, such as the linear learning methods or the manifold learning methods. However, the “crowding” and mixing when reducing dimensions of remote sensing categories could degrade the performance of existing techniques. Then in this paper, by analyzing probability distribution of pairwise distances among remote sensing datapoints, we use the 2-mixed Gaussian model(GMM) to improve the effectiveness of the theory of t-Distributed Stochastic Neighbor Embedding (t-SNE). A basic reducing dimensional model is given to test our proposed methods. The experiments show that the new probability distribution capable retains the local structure and significantly reveals differences between categories in a global structure.
