This paper tackles the problem of image-based indoor location recognition. The context of the present work is activity monitoring using a wearable video camera data. Because application constraints necessitate weak supervision, a semi-supervised approach has been adopted which leverages the large amount of unlabeled images. The proposed method is based on the Bag of Features approach for image description followed by spectral dimensionality reduction in a transductive setup. Additional information from geometrical verification constraints are also considered which allowed to reach higher performance levels. The considered algorithms are compared experimentally on the data acquired in the wearable camera setup.