التفاصيل البيبلوغرافية
| العنوان: |
Local-LDA: Open-Ended Learning of Latent Topics for 3D Object Recognition. |
| المؤلفون: |
Kasaei SH, Lopes LS, Tome AM |
| المصدر: |
IEEE transactions on pattern analysis and machine intelligence [IEEE Trans Pattern Anal Mach Intell] 2020 Oct; Vol. 42 (10), pp. 2567-2580. Date of Electronic Publication: 2019 Jul 02. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: IEEE Computer Society Country of Publication: United States NLM ID: 9885960 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-3539 (Electronic) Linking ISSN: 00985589 NLM ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: |
Original Publication: [New York] IEEE Computer Society. |
| مستخلص: |
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. |
| تواريخ الأحداث: |
Date Created: 20190709 Latest Revision: 20200904 |
| رمز التحديث: |
20221213 |
| DOI: |
10.1109/TPAMI.2019.2926459 |
| PMID: |
31283495 |
| قاعدة البيانات: |
MEDLINE |
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