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Object classification is a highly important area of computer vision and has many applications including robotics, searching images, face recognition, aiding visually impaired people, censoring images and many more. A new common method of classification that uses features is the Bag of Words approach. In this method a codebook of visual words is created using various clustering methods. For increasing the performance Multiple Dictionaries BoW (MDBoW) method that uses more visual words from different independent dictionaries instead of adding more words to the same dictionary was implemented using hard clustering method. Nearest-neighbor assignments are used in hard clustering of features. A given feature may be nearly the same distance from two cluster centers. For a typical hard clustering method, only the slightly nearer neighbor is selected to represent that feature. Thus, the ambiguous features are not well-represented by the visual vocabulary. To address this problem, soft clustering model based Multiple Dictionary Bag of Visual words for image classification is implemented with dictionary generated using modified Fuzzy C-means algorithm using R1 norm. A performance evaluation on images has been done by varying the dictionary size. The proposed method works better when the number of topics and the number of images per topics are more. The results obtained indicate that multiple dictionary bag of words model using fuzzy clustering increases the recognition performance than the baseline method

SOFT CLUSTERING BASED EXPOSITION TO MULTIPLE DICTIONARY BAG OF WORDS