Object identification method based on context information propagation local regression kernel

Abstract

The invention discloses an object identification method based on a context information propagation local regression kernel, comprising steps of obtaining local structure information in a given template image by utilizing a local regression kernel, giving consideration to the context information of the local area, searching a characteristic area similar to a template image in the object image through the correlation between context information and the local information, based on the above arrangement, utilizing a local reservation projection to perform dimensionality reduction on image characteristics in order to reduce the redundancy of the characteristic data quantity, adopting similarity among correlated characters of cosine matrix similarity measurement to find similar character areas, and, at last, utilizing a non-maximum value suppression method to suppress unremarkable area characteristics to obtain a final recognition. The object identification method based on a context information propagation local regression kernel improves accuracy and efficiency of the target identification.

Description

technical field [0001] The invention belongs to the field of image understanding, and in particular relates to a target recognition method based on context information propagating a local regression kernel. Background technique [0002] Target recognition plays an important role in the understanding and analysis of night vision images (including low-light, infrared images), and it also plays an important role in machine vision applications. [0003] Document 1 (Dalal N, Triggs B.Histograms of oriented gradients for human detection, Computer Vision and Pattern Recognition, 2005, 1:886-893.) and others proposed a method of oriented gradient histogram, which uses the oriented gradient histogram , can effectively identify the target, but the low-light image does not recognize the target very well, and the noise is serious. [0004] Document 2 (P.F.Felzenszwalb, R.B.Girshick, D.McAllester, D.Ramanan.Object detection with discriminatively trained part-based models, Pattern Analys...

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Problems solved by technology

[0003] Document 1 (Dalal N, Triggs B.Histograms of oriented gradients for human detection, Computer Vision and Pattern Recognition, 2005, 1:886-893.) and others proposed a method of oriented gradient histogram, which uses the oriented gradient histogram , can effectively identify the target, but the low-light image does not recognize the target very well, and the noise is serious

[0004] Document 2 (P.F.Felzenszwalb, R.B.Girshick, D.McAllester, D.Ramanan.Object detection with discriminatively trained part-based models, Pattern Analysis and Machine Intelligence, vol.32, pp.1627-1645, 2010.) et al proposed The encoding training method can effectively identify the target by using the trained discriminant model, but it needs training samples, a large amount of data, and complex calculations

[0005] Document 3 (H.J.Seo, P.Milanfar. Training-free, Generic Object Detection using Locally Adaptive Regression Kernels, IEEE Trans on Pattern Analysis and Machine Intelligence, vol.32, no.9, pp.1688-1704, 2010.) using The local regression kernel method, but only uses the local regression kernel without considering the context information, and a large number of unrecognized targets are prone to appear in the recognition process

Therefore, the recognition efficiency is low and cannot be better applied to low-light images

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