Joint spatial-depth feature pooling for RGB-D object classification
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Joint spatial-depth feature pooling for RGB-D object classification. / Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping.
Image analysis: 19th Scandinavian Conference, SCIA 2015, Copenhagen, Denmark, June 15-17, 2015. Proceedings. red. / Rasmus R. Paulsen; Kim S. Pedersen. Springer, 2015. s. 314-326 (Lecture notes in computer science, Bind 9127).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Joint spatial-depth feature pooling for RGB-D object classification
AU - Pan, Hong
AU - Olsen, Søren Ingvor
AU - Zhu, Yaping
N1 - Conference code: 19
PY - 2015
Y1 - 2015
N2 - RGB-D camera can provide effective support with additional depth cue for many RGB-D perception tasks beyond traditional RGB information. However, current feature representations based on RGB-D camera utilize depth information only to extract local features, without considering it for the improvement of robustness and discriminability of the feature representation by merging depth cues into feature pooling. Spatial pyramid model (SPM) has become the standard protocol to split 2D image plane into sub-regions for feature pooling in RGB-D object classification. We argue that SPM may not be the optimal pooling scheme for RGB-D images, as it only pools features spatially and completely discards the depth topological information. Instead, we propose a novel joint spatial-depth pooling scheme (JSDP) which further partitions SPM using the depth cue and pools features simultaneous in 2D image plane and the depth direction. Embedding the JSDP with the standard feature extraction and feature encoding modules, we achieve superior performance to the state-of-the-art methods on benchmarks for RGB-D object classification and detection.
AB - RGB-D camera can provide effective support with additional depth cue for many RGB-D perception tasks beyond traditional RGB information. However, current feature representations based on RGB-D camera utilize depth information only to extract local features, without considering it for the improvement of robustness and discriminability of the feature representation by merging depth cues into feature pooling. Spatial pyramid model (SPM) has become the standard protocol to split 2D image plane into sub-regions for feature pooling in RGB-D object classification. We argue that SPM may not be the optimal pooling scheme for RGB-D images, as it only pools features spatially and completely discards the depth topological information. Instead, we propose a novel joint spatial-depth pooling scheme (JSDP) which further partitions SPM using the depth cue and pools features simultaneous in 2D image plane and the depth direction. Embedding the JSDP with the standard feature extraction and feature encoding modules, we achieve superior performance to the state-of-the-art methods on benchmarks for RGB-D object classification and detection.
U2 - 10.1007/978-3-319-19665-7_26
DO - 10.1007/978-3-319-19665-7_26
M3 - Article in proceedings
SN - 978-3-319-19664-0
T3 - Lecture notes in computer science
SP - 314
EP - 326
BT - Image analysis
A2 - Paulsen, Rasmus R.
A2 - Pedersen, Kim S.
PB - Springer
Y2 - 15 June 2015 through 17 June 2015
ER -
ID: 141048055