Title: Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning
|
| Authors: Li, B; Yuan, CF; Xiong, WH; Hu, WM; Peng, HW; Ding, XM; Maybank, S
|
| Author Full Names: Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve
|
| Source: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 39 (12):2554-2560; 10.1109/TPAMI.2017.2669303 DEC 2017
|
| Language: English
|
| Abstract: In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm ((MIL)-I-2) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse epsilon-graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M2IL. Experiments and analyses in many practical applications prove the effectiveness of the M2IL.
|
| ISSN: 0162-8828
|
| eISSN: 1939-3539
|
| IDS Number: FL6ZQ
|
| Unique ID: WOS:000414395400017
|
| PubMed ID: 28212079
*Click Here to View Full Record
