Image Classification Using Probability Higher-Order Local Auto-Correlations

Abstract. In this paper, we propose a novel method for generic object recognition by using higher-order local auto-correlations on probability images. The proposed method is an extension of bag-of-features approach to posterior probability images. Standard bag-of-features is approximately thought as sum of posterior probabilities on probability images, and spatial co-occurrences of posterior probability are not utilized. Thus, its descriptive ability is limited. However, using local auto-correlations of probability images, the proposed method extracts richer information than the standard bag-of-features. Experimental results show the proposed method is enable to have higher classification performances than the standard bag-of-features.

• Type: Conference
• Year: 2009
• Where: ACCV
• Authors: Tetsu Matsukawa, Takio Kurita
• Keywords: ACCV 2009, Computer Vision, Posterior Probability, Posterior Probability Images, Probability Images