  Image Classification based on BOW (Bag-of-words) has broad application
prospect in pattern recognition field but the shortcomings are existed because
of single feature and low classification accuracy. To this end we combine three
ingredients: (i) Three features with functions of mutual complementation are
adopted to describe the images, including PHOW (Pyramid Histogram of Words),
PHOC (Pyramid Histogram of Color) and PHOG (Pyramid Histogram of Orientated
Gradients). (ii) The improvement of traditional BOW model is presented by using
dense sample and an improved K-means clustering method for constructing the
visual dictionary. (iii) An adaptive feature-weight adjusted image
categorization algorithm based on the SVM and the fusion of multiple features
is adopted. Experiments carried out on Caltech 101 database confirm the
validity of the proposed approach. From the experimental results can be seen
that the classification accuracy rate of the proposed method is improved by
7%-17% higher than that of the traditional BOW methods. This algorithm makes
full use of global, local and spatial information and has significant
improvements to the classification accuracy.
