This paper describes an approach to vessel classification from satellite images using content based image retrieval methodology. Content-based image retrieval is an important problem in both medical imaging and surveillance applications. In many cases the archived reference database is not fully structured, thus making content-based image retrieval a challenging problem. In addition, in surveillance applications, the query image may be affected by weather or/and geometric distortions. Our approach of content-based vessel image retrieval consists of two phases. First, we create a structured reference database, then for each new query image of a vessel we find the closest cluster of images in the structured reference database, thus identifying and classifying the vessel. Then we update the closest cluster with new query image.
This paper describes an approach to identify individuals with suspicious objects in a crowd. To
accomplish this goal we define criteria for a suspicious individual we are searching for. The query image is declared to
contain a suspicious individual if it satisfies these criteria. In our implementation we apply a well-known algorithm
suite used in image retrieval, mobile visual search problems where the reference data base of images is stored in a
hierarchical tree data structure. In many cases, the construction of such a hierarchical tree uses k-means clustering
followed by geometric verification. However, the number of clusters is not known in advance, and sometimes it is
randomly generated. This may lead to congested clustering which can cause problems in grouping large real-time data.
To overcome this problem, in this work, we estimate the number of clusters using the Indian Buffet stochastic process.
We present examples illustrating our method.