SemiAutomatic Photo Clustering with Distance Metric Learning Photo clustering has been widely explored in many applications such as album management. But automatic clustering can hardly achieve satisfying performance due to the large variety of photosu0027 content. This paper proposes a semiautomatic photo clustering scheme that attempts to improve clustering performance with usersu0027 interactions. Users can adjust the results of automatic clustering, and a set of constraints among photos are generated accordingly. A distance metric is then learned with these constraints and we can reimplement clustering with this metric. We conduct experiments on different photo albums, and experimental results have demonstrated that our approach is able to improve automatic photo clustering results, and it is better than pure manual adjustment approach by exploring distance metric learning.

Interactive Important Social Character Identification from Large Photo Collections In the paper, we describe a mechanism to identify important social characters by analyzing the social structures embedded in photo collections. We first construct a weighted undirected graph from photo collections by examining the coappearance of individuals in photos, wherein the weights of edges are measures of the social closeness of the involved individuals (vertices in the graph). Then a graph clustering algorithm that maximizes the modularity of the graph partition is applied to detect the embedded social clusters. Once the social clusters are identified, we measure individualu0027s contribution to the formation of a social cluster to quantify the social importance of each character in the cluster. To compensate the discrepancy between the userperceived important social characters and the algorithmically computed ones, we propose an interactive browsing scheme to enable viewers quickly identify import social characters that conform to their subjectivity. The effectiveness of the proposed mechanism is demonstrated through experiments on consumer photo collections.