Object search using object co-occurrence relations derived from web content mining

We present the novel framework of knowledge construction (ICC: Independent Co-occurring based Construction) based on co-occurrence relations of objects. We compare its characteristics with that of general approach (DCC: Dependent Co-occurring based Construction) in various construction aspects: variations of trained probability values, percentage differences (probability value and priority ranking order), and reconstruction time. The similarity of their data content and faster reconstruction time of ICC suggest that ICC is more suitable for applications of service robot. Instead of using visual feature, we employed annotated data, such as word-tagging images, as the training set to increase the accuracy of correspondence between related keywords and images. The task of object search in unknown environment is selected to evaluate the applicability of using constructed knowledge (OCR: Object Co-occurrence Relations). We explore the search behaviors, provided by OCR-based search (indirect search) and greedy search (direct search), in simulation experiments with five different starting robot positions. Their search behaviors are also compared from the aspects of consumed computational time, travel distance, and number of visited locations. The certainty of success of OCR-based search assures us of its benefit. Moreover, the object search experiment in unknown human environment is conducted by a mobile robot, equipped with a stereo camera, to show the possibility of using OCR in the search in real world.