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Semantic attributes have been proved to be an effective representation approach for image classification in recent years. However, most of the existing semantic attributes are explicitly pre-defined and the attribute number is also very limited in practice. Therefore, we often encounter difficulties by using a fixed incomplete semantic attribute set for different classification tasks. One possible solution is to expand the semantic attribute representation with some non-semantic mid-level features. However, how to make the expanded features more effective and discriminative is still seldom exploited. In this paper, we propose a Sequential augmented Attributes learning (SAL) method to implement semantic attribute augmentation. In our SAL method, the non-semantic mid-level features are learned one by one under a sequential error-correcting scheme so that we can obtain more discriminating power with very compact expanded features. Extensive experiments are conducted on two public datasets and the results show that our approach achieves encouraging performance.

This paper presents a lifecycle model (LCM) to simulate bacterial evolution from a finite population of Escherichia coli (E. coli) bacteria. The potential of this approach is in relating the microscopic behaviors of single bacterial cell to the macroscopic effects of bacterial colonies. This can be accomplished via use of an individual-based modeling method under the framework of agent-environment-rule (AER). Here, our study focuses on investigating the behaviors at different developmental stages in E. coli lifecycle and developing a new biologically inspired methodology for static or dynamic systems. The experimental results through a varying environment demonstrates that our model can be used to study under which circumstances a certain bacterial behaviors emerges, and also give an inspiration to design a new biological optimization algorithm being used for optimization problems.