IEEE Trans Image Process - Image classification via object-aware holistic superpixel selection.

Tópicos

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Resumo

In this paper, we propose an object-aware holistic superpixel selection (HPS) method to automatically select the discriminative superpixels of an image for image classification purpose. Through only considering the selected superpixels, the interference of cluttered background on the object can be alleviated effectively and thus the classification performance is significantly enhanced. In particular, for an image, HPS first selects the discriminative superpixels for the characteristics of certain class, which can together match the object template of this class well. In addition, these superpixels compose a class-specific matching region. Through performing such superpixel selection for several most probable classes, respectively, HPS generates multiple class-specific matching regions for a single image. Then, HPS merges these matching regions into an integral object region through exploiting their pixel-level intersection information. Finally, such object region instead of the original image is used for image classification. An appealing advantage of HPS is the ability to alleviate the interference of cluttered background yet not require the object to be segmented out accurately. We evaluate the proposed HPS on four challenging image classification benchmark datasets: Oxford-IIIT PET 37, Caltech-UCSD Birds 200, Caltech 101, and PASCAL VOC 2011. The experimental results consistently show that the proposed HPS can remarkably improve the classification performance.

Resumo Limpo

paper propos objectawar holist superpixel select hps method automat select discrimin superpixel imag imag classif purpos consid select superpixel interfer clutter background object can allevi effect thus classif perform signific enhanc particular imag hps first select discrimin superpixel characterist certain class can togeth match object templat class well addit superpixel compos classspecif match region perform superpixel select sever probabl class respect hps generat multipl classspecif match region singl imag hps merg match region integr object region exploit pixellevel intersect inform final object region instead origin imag use imag classif appeal advantag hps abil allevi interfer clutter background yet requir object segment accur evalu propos hps four challeng imag classif benchmark dataset oxfordiiit pet caltechucsd bird caltech pascal voc experiment result consist show propos hps can remark improv classif perform

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