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Removing photobombing elements from images is a challenging task that requires sophisticated image inpainting techniques. Despite the availability of various methods, their effectiveness depends on the complexity of the image and the nature of the distracting element. To address this issue, we conducted a benchmark study to evaluate 10 state-of-the-art photobombing removal methods on a dataset of over 300 images. Our study focused on identifying the most effective image inpainting techniques for removing unwanted regions from images. We annotated the photobombed regions that require removal and evaluated the performance of each method using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). The results show that image inpainting techniques can effectively remove photobombing elements, but more robust and accurate methods are needed to handle various image complexities. Our benchmarking study provides a valuable resource for researchers and practitioners to select the most suitable method for their specific photobombing removal task.

 

The price of a house depends on many factors, such as its size, location, amenities, surrounding establishments, and the season in which the house is being sold, just to name a few of them. As a seller, it is absolutely essential to price the property competitively else it will not attract any buyers. This problem has given rise to multiple companies as well as past research works that try to enhance the predictability of property prices using relevant mathematical models and machine learning techniques. In this research, we investigate the usage of machine learning in predicting the house price based on related estate attributes and visual images. To this end, we collect a dataset of 2,000 houses across different cities in the United States. For each house, we annotate 14 estate attributes and five visual images for exterior, interior-living room, kitchen, bedroom, and bathroom. Following the dataset collection, different features are extracted from the input data. Furthermore, a multi-kernel regression approach is used to predict the house price from both visual cues and estate attributes. The extensive experiments demonstrate the superiority of the proposed method over the baselines.