Auto-selected algorithm of the neighborhood size and intrinsic dimension for image feature extraction

作者：Liu Feng Lian^*; Wang Ri Wei; Cheng Jun; Kang Juan Peng

来源：Journal of Optoelectronics - Laser, 2013, 24(12): 2416-2420.

• Dimensionality reduction algorithms
• Image feature extractions
• Image features
• Intrinsic dimensions
• Low computational complexity
• Manifold learning
• Nearest neighbor distance
• Neighborhood size

Dimensionality reduction algorithms have been applied widely in computer vision, medical image processing, video processing, face recognition, image retrieval etc. However, some problems need to be fixed. First of all, how to get the domain size and intrinsic dimension is first primary problem to be fixed, which is seriously restricted the rapid development. Traditonal method used the K-Nearest Neighbor to search the neighborhoods of each image sample. But it costs much time sometimes. In this paper, we aim at the problem of finding intrinsic structure and domain size automatically for high dimensional image data. We present a new technique which can get intrinsic dimension and neighborhood size automatically and adaptively. The algorithm can be used for extracting local features from images. Firstly, we made linear reconstruction based on the nearest neighbor distance for image feature extraction, and optimize the distribution on whole manifold, then we get expression function in which variable is the optimal linear reconstruction for locally low dimensional feature. Lastly, minimiing the variance of the function is to get aotomatic selection strategy. Experiments show that the algorithm is not only simple but also high matching rate and low computational complexity.

• 出版日期2013
• 单位天津大学