Local-LDA: Open-Ended Learning of Latent Topics for 3D Object Recognition

Service robots are expected to be more autonomous and work effectively in human-centric environments. This implies that robots should have special capabilities, such as learning from past experiences and real-time object category recognition. This paper proposes an open-ended 3D object recognition s...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2020-10, Vol.42 (10), p.2567-2580
Main Authors: Kasaei, S. Hamidreza, Lopes, Luis Seabra, Tome, Ana Maria
Format: Article
Language:English
Subjects:
3D object learning and recognition
Computing time
Dirichlet problem
Latent dirichlet allocation (LDA)
Learning
local-LDA
object perception
Object recognition
open-ended learning
Robots
Semantics
Service robots
Shape
Task analysis
Three-dimensional displays
Training
Visualization
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Summary:Service robots are expected to be more autonomous and work effectively in human-centric environments. This implies that robots should have special capabilities, such as learning from past experiences and real-time object category recognition. This paper proposes an open-ended 3D object recognition system which concurrently learns both the object categories and the statistical features for encoding objects. In particular, we propose an extension of Latent Dirichlet Allocation to learn structural semantic features (i.e., visual topics), from low-level feature co-occurrences, for each category independently. Moreover, topics in each category are discovered in an unsupervised fashion and are updated incrementally using new object views. In this way, the advantages of both the (hand-crafted) local features and the (learned) structural semantic features have been considered and combined in an efficient way. An extensive set of experiments has been performed to assess the performance of the proposed Local-LDA in terms of descriptiveness, scalability, and computation time. Experimental results show that the overall classification performance obtained with Local-LDA is clearly better than the best performances obtained with the state-of-the-art approaches. Moreover, the best scalability, in terms of number of learned categories, was obtained with the proposed Local-LDA approach, closely followed by a Bag-of-Words (BoW) approach. Concerning computation time, the best result was obtained with BoW, immediately followed by the Local-LDA approach.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2019.2926459