In order to retrieve images of traffic violations from a large number of road traffic images， a semantic retrieval method based on specific object self-recognition was proposed. Firstly， road traffic domain ontology as well as road traffic rule description were established by experts in traffic domain. Secondly， traffic image features were extracted by Convolutional Neural Network （CNN）， and combined with the strategy for classifying image features which is based on the proposed improved Support Vector Machine based Decision Tree （SVM-DT） algorithm， the specific objects and the spatial positional relationship between the objects in the traffic images were automatically recognized and mapped into the association relationship （rule instance） between the corresponding ontology instance and its objects. Finally， the image semantic retrieval result was obtained by reasoning based on ontology instances and rule instances. Experimental results show that the proposed method has higher accuracy， recall and retrieval efficiency compared to keyword and ontology traffic image semantic retrieval methods.