In this paper, I propose and explore a method for image location classification. Most existing works concentrate on outdoor scenes as scenery or an iconic landmark make it easier to point out the location. Few researchers have addressed the issue of indoor scenes. Although indoor images increase the difficulty of tracking geolocation, it is necessary to respond to this shortcoming as many crimes happen indoors.
To address this problem, I propose a method for indoor image location classification by segmenting patterns of extracted objects from images. Specifically, I extract objects from images. Then, based on the accuracy levels of the bounding boxes of specific kinds of objects in the image, I only crop that kind of objects from original images. Moreover, I segment patterns from the extracted objects and crop those patterns by thresholding techniques. To classify images by these segmented patterns, I employ convolutional neural networks. Experimental results in the dataset of hotel rooms across the globe show promising accuracies, which witnesses that my method contributes to ultimately identifying the hotel chain which the image belongs to from the hotel dataset.