IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a scholarly archival journal published monthly. This journal covers traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Read the full scope of TPAMI.
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From the September 2017 issue
Multi-Instance Classification by Max-Margin Training of Cardinality-Based Markov Networks
By Hossein Hajimirsadeghi and Greg Mori
We propose a probabilistic graphical framework for multi-instance learning (MIL) based on Markov networks. This framework can deal with different levels of labeling ambiguity (i.e., the portion of positive instances in a bag) in weakly supervised data by parameterizing cardinality potential functions. Consequently, it can be used to encode different cardinality-based multi-instance assumptions, ranging from the standard MIL assumption to more general assumptions. In addition, this framework can be efficiently used for both binary and multiclass classification. To this end, an efficient inference algorithm and a discriminative latent max-margin learning algorithm are introduced to train and test the proposed multi-instance Markov network models. We evaluate the performance of the proposed framework on binary and multi-class MIL benchmark datasets as well as two challenging computer vision tasks: cyclist helmet recognition and human group activity recognition. Experimental results verify that encoding the degree of ambiguity in data can improve classification performance.
Editorials and Announcements
- We are pleased to announce that Sven Dickinson, a professor in the Department of Computer Science at the University of Toronto, Canada, has been named the new Editor-in-Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence starting in 2017.
- According to Thomson Reuters' 2013 Journal Citation Report, TPAMI has an impact factor of 5.694.
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