When the dimensionality of the semantic attributes is limited

it is difficult for attribute-based zero-shot image classifiers to distinguish the objects with similar attributes.Aiming at the limitation of describing objects with semantic attributes

an improved direct attribute prediction(DAP) model for zero-shot image classifying based on hybrid attribute(HA) is proposed

which is called HA-DAP.At first

we carry out the sparse coding on the low-level features to obtain the non-semantic attributes that are used to assist the existing semantic attributes.Then

we take the hybrid attributes including the learned non-semantic attributes and the manually specified semantic attributes as the mid-layer of DAP model and use the idea of attribute prediction to train the hybrid attribute-based classifier.At last

according to the predicted hybrid attributes and the relationship between the attributes and classes

we can recognize the class label for the testing sample.Experimental results on the OSR

Pub Fig and Shoes datasets show that

the HA-DAP outperforms the DAP in the classification performance

i.e.

when compared with the DAP

the proposed HA-DAP yields much higher zero-shot image classification accuracy and AUC value.