IEEE Trans Image Process - Object detection with DoG scale-space: a multiple kernel learning approach.

Tópicos

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Resumo

Difference of Gaussians (DoG) scale-space for an image is a significant way to generate features for object detection and classification. While applying DoG scale-space features for object detection/classification, we face two inevitable issues: dealing with high dimensional data and selecting/weighting of proper scales. The scale selection process is mostly ad-hoc to date. In this paper, we propose a multiple kernel learning (MKL) method for both DoG scale selection/weighting and dealing with high dimensional scale-space data. We design a novel shift invariant kernel function for DoG scale-space. To select only the useful scales in the DoG scale-space, a novel framework of MKL is also proposed. We utilize a 1-norm support vector machine (SVM) in the MKL optimization problem for sparse weighting of scales from DoG scale-space. The optimized data-dependent kernel accommodates only a few scales that are most discriminatory according to the large margin principle. With a 2-norm SVM this learned kernel is applied to a challenging detection problem in oil sand mining: to detect large lumps in oil sand videos. We tested our method on several challenging oil sand data sets. Our method yields encouraging results on these difficult-to-process images and compares favorably against other popular multiple kernel methods.

Resumo Limpo

differ gaussian dog scalespac imag signific way generat featur object detect classif appli dog scalespac featur object detectionclassif face two inevit issu deal high dimension data selectingweight proper scale scale select process most adhoc date paper propos multipl kernel learn mkl method dog scale selectionweight deal high dimension scalespac data design novel shift invari kernel function dog scalespac select use scale dog scalespac novel framework mkl also propos util norm support vector machin svm mkl optim problem spars weight scale dog scalespac optim datadepend kernel accommod scale discriminatori accord larg margin principl norm svm learn kernel appli challeng detect problem oil sand mine detect larg lump oil sand video test method sever challeng oil sand data set method yield encourag result difficulttoprocess imag compar favor popular multipl kernel method

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