A manifold ranking based method using hybrid features for crime scene shoeprint retrieval

Shoeprints are frequently acquired at crime scenes and can provide vital clues for investigation. How to retrieve the most similar shoeprints available in the dataset to the highly degraded query crime scene shoeprint is a challenging work. Some existing shoeprint retrieval algorithms cannot work well with highly degraded shoeprint image on a large scale dataset, and the results are not well correlated with the forensic experts. This study proposes a manifold ranking based method using hybrid features of region and appearance to improve the retrieval performance. Manifold ranking method is introduced to narrow the well-known gap between visual features and semantic concepts. We define the ranking cost function from three perspectives: (i) the feature similarity between the query and the dataset images, (ii) the relationship between every two shoeprints in the dataset, (iii) the assigned opinion scores for multiple shoeprints left in one crime scene by the forensic expert. Experiments on the real crime scene datasets show that the cumulative match score of the proposed method is more than 93.5 % on the top 2 % of the dataset composed of 10096 crime scene shoeprints.