Objective Shoeprints serve as vital evidence in forensic investigations, and determining how a massive amount of shoeprint images can be clustered automatically through long-term accumulation has become one of the urgent tasks of criminal technology. Unlike other image sets, the number of shoeprint categories is not only considerable but also unknown. Shoeprint images in the feature space are distributed inhomogeneously and sparsely, but the quantity of each class is low. For these reasons, most existing clustering algorithms cannot satisfactorily cluster shoeprints. In this study, an automatic clustering method is proposed to divide shoeprint sets effectively based on an analysis of the distribution of shoeprint images in the feature space. Method Through statistics on labeled shoeprint-image databases, we found that shoeprint sets of different patterns do not intersect, and a blank region, where no shoeprints exist, is present between every two classes. Blank regions are called margins in this paper. The core objective of the proposed algorithm is to determine the margins between classes and use them to divide a shoeprint set. The process involves the following steps:1) dividing the shoeprint set with monotonically increasing or descending thresholds, which are used to classify two shoeprint images into the same cluster; 2) searching for the cluster that does not change with K consecutive partitions; 3) outputting the stable cluster and removing the shoeprints belonging to the output stable cluster from the dataset; 4) choosing the next threshold and dividing the remaining dataset; 5) returning to step 2) until the remaining set is empty. Result Experimental results on two kinds of publicly available databases and one real shoeprint database which comprises 5792 images, have shown that the proposed algorithm outperforms state-of-the-art clustering algorithms on common clustering evaluation 

Objective Shoeprints serve as vital evidence in forensic investigations, and determining how a massive amount of shoeprint images can be clustered automatically through long-term accumulation has become one of the urgent tasks of criminal technology. Unlike other image sets, the number of shoeprint categories is not only considerable but also unknown. Shoeprint images in the feature space are distributed inhomogeneously and sparsely, but the quantity of each class is low. For these reasons, most existing clustering algorithms cannot satisfactorily cluster shoeprints. In this study, an automatic clustering method is proposed to divide shoeprint sets effectively based on an analysis of the distribution of shoeprint images in the feature space. Method Through statistics on labeled shoeprint-image databases, we found that shoeprint sets of different patterns do not intersect, and a blank region, where no shoeprints exist, is present between every two classes. Blank regions are called margins in this paper. The core objective of the proposed algorithm is to determine the margins between classes and use them to divide a shoeprint set. The process involves the following steps:1) dividing the shoeprint set with monotonically increasing or descending thresholds, which are used to classify two shoeprint images into the same cluster; 2) searching for the cluster that does not change with K consecutive partitions; 3) outputting the stable cluster and removing the shoeprints belonging to the output stable cluster from the dataset; 4) choosing the next threshold and dividing the remaining dataset; 5) returning to step 2) until the remaining set is empty. Result Experimental results on two kinds of publicly available databases and one real shoeprint database which comprises 5792 images, have shown that the proposed algorithm outperforms state-of-the-art clustering algorithms on common clustering evaluation 

Objective Shoeprints serve as vital evidence in forensic investigations, and determining how a massive amount of shoeprint images can be clustered automatically through long-term accumulation has become one of the urgent tasks of criminal technology. Unlike other image sets, the number of shoeprint categories is not only considerable but also unknown. Shoeprint images in the feature space are distributed inhomogeneously and sparsely, but the quantity of each class is low. For these reasons, most existing clustering algorithms cannot satisfactorily cluster shoeprints. In this study, an automatic clustering method is proposed to divide shoeprint sets effectively based on an analysis of the distribution of shoeprint images in the feature space. Method Through statistics on labeled shoeprint-image databases, we found that shoeprint sets of different patterns do not intersect, and a blank region, where no shoeprints exist, is present between every two classes. Blank regions are called margins in this paper. The core objective of the proposed algorithm is to determine the margins between classes and use them to divide a shoeprint set. The process involves the following steps:1) dividing the shoeprint set with monotonically increasing or descending thresholds, which are used to classify two shoeprint images into the same cluster; 2) searching for the cluster that does not change with K consecutive partitions; 3) outputting the stable cluster and removing the shoeprints belonging to the output stable cluster from the dataset; 4) choosing the next threshold and dividing the remaining dataset; 5) returning to step 2) until the remaining set is empty. Result Experimental results on two kinds of publicly available databases and one real shoeprint database which comprises 5792 images, have shown that the proposed algorithm outperforms state-of-the-art clustering algorithms on common clustering evaluation