Location identification using images is an emerging technology with a broad range of potential applications. It can be employed in scenarios such as locating individuals in distress during mountain climbing, aiding in disaster response for events like earthquakes and floods, finding lost children in tourist areas or crowded spaces, and tracking individuals with dementia or similar conditions. However, accurately identifying a location based solely on an image remains a significant challenge, as visual cues related to location are often absent or ambiguous. Despite growing recognition of the importance and potential of this technology, no practical solution has yet been established. In this paper, we propose a novel method for location identification inspired by the concept of collective knowledge. In this approach, a large number of individuals contribute by registering images of objects they subjectively associate with specific locations. The resulting set of object images tends to capture features that others-who did not participate in the registration-also perceive as characteristic of those locations. Therefore, it becomes feasible to estimate a location based on images containing such objects. We present the design and implementation of this method and evaluate its effectiveness through experiments in real-world environments.