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Indentation Mark Feature Extraction Algorithm Based on Local Gradient Directional Ternary Pattern and CNN

In most crime scene or traffic investigations where tire indentation mark images are obtained, they usually provide vital clues in the investigations. However, due to the particularity and scarcity of tire indentation data, there are few studies in tire indentation marks retrieval and no standard database. By observing the complex and rich characteristics of tire texture information in tire indentation mark, a robust feature suitable for this particular image retrieval is proposed. The main contributions include: (1) A texture feature description method based on local gradient direction ternary pattern (LGDTP) is proposed: The texture information is effectively described, which is shown to have higher average precision than other conventional texture description features. (2) The deep features are extracted from the indentation mark images using the pre-trained CNN model VGG-16. A more compact deep features are then obtained by max-pooling and average-pooling on the Pool5 layer. These features are finally integrated with LGDTP, which results in a more accurate tire indentation mark features. Experimental results show that the fused features are more suitable for describing the texture of tire indentation mark images.