Probabilistic and ternary representation of attributes in attribute based object classification

Attribute based approach is a new object classification model. The fundamental difference from traditional models is that it employs an attribute layer in the classifier cascade which serves as a switching entity between low level pixel data and high level object labels. The model brings new insights to object classification that we do not observe with the traditional approaches: classification of unseen images, description of the unclassified objects, description of unexpected attributes, description of missing attributes and learning from textual descriptions. Recent preliminary publications give promising results. However, they are not at desired accuracy levels yet. In this work, effects of ternary and probabilistic representations of attributes instead of binary on classification performance are evaluated.