 To address this challenge , we propose the use of topologically persistent features for object recognition. We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features,  by applying persistent homology to  filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
<meaning-changed>  To address this challenge , we propose the use of topologically persistent features for object recognition. We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features,  by applying persistent homology to  filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
 To address this challenge , we propose the use of topologically persistent features , which rely on the shape information of the objects, to address this challenge. In particular, we extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features, by applying persistent homology to filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .