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Object recognition in a large scale collection of images has become an important application in machine vision. The recent advances in the object or image recognition for classification of objects shows that Bag-of-visual words approach is a better method for image classification problems. An object recognition method based on the Bag-of-Words (BoW) model is implemented were descriptors are quantized to form a visual word dictionary called codebook with the help of soft clustering algorithm. To increase the recognition rate and accuracy of detection, the concept of Multiple Dictionary Bag of Words model (MDBoW) is implemented in which the dictionaries built using soft clustering algorithm from different subsets of the features are combined. The performances of existing BoW model with fuzzy codebook and the proposed MDBoW are evaluated in terms of macro precision, micro precision, accuracy and F1 measure. The proposed algorithm gives an increased recognition rate and accuracy of detection

Fuzzy based Multiple Dictionary Bag of Words for Image Classification