1. A method comprising: selecting, by a processor implementing a first classifier, a first template object based on a first plurality of visual features after the first plurality of visual features were extracted by a first visual feature extractor based on an image of the selected object, the selected object being an instance of a particular template object of a plurality of template objects; selecting, by the processor implementing a second classifier, a second template object based on a second plurality of visual features after the second plurality of visual features were extracted by a second visual feature extractor based on the image of the selected object; selecting, by the processor implementing a third classifier, a third template object based on a visual word count vector that represents counts of visual words after the visual word count vector was determined by a visual word count vector algorithm based on the image of the selected object; and classifying the selected object as an instance of the particular template object based on said selecting of the first template object, said selecting of the second template object, and said selecting of the third template object, wherein said classifying the selected object as the instance of the particular template object includes: when the first template object, the second template object, and the third template object are all a same single template object, classifying the selected object as the instance of the particular template object, wherein the same single template object is the particular template object; and when the first template object, the second template object, and the third template object are not all a same single template object, classifying the selected object as the instance of the particular template object based on results of comparisons of the selected object with the first template object, the second template object, and the third template object, the results indicating that the selected object matches the particular template object.