an efficient method of image identification by combining image features
This paper proposes an efficient image identification method by combining image features and using image clustering. For more efficient image identification, we use global and local features in a hierarchical manner. The combined global feature reflecting general information of image helps faster retrieval of candidate images and the feature point based local feature facilitates more accurate fine matching with the candidate images. We consider the Fuzzy C-Means clustering method since it is effective for the image data which are characteristically alike and have fuzzy boundary in coordinate by their global features. The global feature vector which we use is very effective in clustering and retrieval since it represents general properties of image and its dimension is very low. As a result, the number of fine matching which requires very large computing time and high complexity is considerably decreased since searching original image of query is done by fine matching within partial database of candidate images.
image identification clustering local feature global feature feature point descriptor interest point image retrieval identifier