This paper aims to detect compositional features from room images and present appropriate compositions for attractive photographic assistance. In this paper, we define the boundaries between walls and floor, the boundaries between walls and ceiling as compositional feature lines and vanishing points as compositional feature points. The proposed method was able to properly detect the composition of 27 images out of 36 room images. Especially for the two vanishing point images, the detection was successful in more than 80% of the images. Vanishing points were detected in all the room images with one and two vanishing points. We proposed a method that can easily take attractive indoor images by presenting an appropriate composition by detecting the compositional features. With this composition and features, a photography support system would be promoted to take effective room images easily. As a feature works, this method will be optimized to real time and interactive support for shooting seen.
With the growing mobility of the population and popularity of the Internet, real estate agents have larger database to manage. This paper presents a solution to classify images of a certain house, such as living room, kitchen, bathroom, layout sketch and external appearance collected by a real estate agent using transfer learning. The pictures are like those images posted on the real estate agent website to help people find out what’s the house looks like inside and outside. We employ a transfer learning approach for VGG-19 architecture. Using a network pre-trained on the general ImageNet dataset, we perform supervised fine-tuning on the last full connect layer and change the output size from 1000 to 5. Experimental results achieved with 5-fold cross-validation show that after training, this fine-tuning approach achieves high test accuracy of 99.4%.
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