A Study of Household Object Recognition Using SIFT-Based Bag-of-Words Dictionary and SVMs

Sampath, A.; Sivaramakrishnan, Aravind; Narayan, Keshav; Aarthi, R.

doi:10.1007/978-81-322-2671-0_55

Cited by 4 publications

(1 citation statement)

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“…General methods on objects classification are based on the Bag of Words model (BOW) [2][3] to construct a visual vocabulary. The BOW is a popular form of object representation well applied in the near-duplicate/planar images.…”

Section: Introductionmentioning

confidence: 99%

Objects Classification for Mobile Robots Using Hierarchic Selective Search Method

Cao

et al. 2016

MATEC Web Conf.

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Abstract. Aiming at determining the category of an image captured from mobile robots for intelligent application, classification with the bag-of-words model is proved effectively in near-duplicate/planar images. When it comes to images from mobile robots with complex background, does it still work well? In this paper, based on the merging criterion improvement, a method named hierarchical selective search is proposed hierarchically extracting complementary features to form a combined and environment-adaptable similarity measurement for segmentation resulting a small and high-quality regions set. Simultaneously those regions rather than a whole image are used for classification. As a result, it well improved the classification accuracy and make the bog-of-word model still work well on classification for mobile robots. The experiments on hierarchical selective search show its better performance than selective search on two task datasets for mobile robots. The experiments on classification shows the samples from regions are better than those original whole images. The advantage of less quantity and higher quality object regions from hierarchical selective search is more prominent when it comes to those special tasks for mobile robots with scarce data.

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Section: Introductionmentioning

confidence: 99%