We address the problem of food items detection and recognizing different food categories in images. Given the variety of food items with low inter- and high intraclass variations and the limited information contained in a single image, the problem is known to be particularly hard. In order to achieve better detection and recognition capabilities, we propose a joint use of multiple classifiers trained on features extracted via multiple deep models using different fusion techniques, including an early and two different late fusion schemes, namely induced order weighted averaging and particle swarm optimization based fusion. Moreover, we assess the performance of different deep models in food items detection and recognition. Experimental evaluations are carried out on two large-scale benchmark datasets, demonstrating better results for the proposed approach.
Over the last few years, a rapid growth has been witnessed in the number of digital photos produced per year. This rapid process poses challenges in the organization and management of multimedia collections, and one viable solution consists of arranging the media on the basis of the underlying events. However, album-level annotation and the presence of irrelevant pictures in photo collections make event-based organization of personal photo albums a more challenging task. To tackle these challenges, in contrast to conventional approaches relying on supervised learning, we propose a pipeline for event recognition in personal photo collections relying on a multiple instance-learning (MIL) strategy. MIL is a modified form of supervised learning and fits well for such applications with weakly labeled data. The experimental evaluation of the proposed approach is carried out on two large-scale datasets including a self-collected and a benchmark dataset. On both, our approach significantly outperforms the existing state-of-the-art.
This paper introduces a new application of computer vision. To the best of the author’s knowledge, it is the first attempt to incorporate computer vision techniques into room interior designing. The computer vision based interior designing is achieved in two steps: object identification and color assignment. The image segmentation approach is used for the identification of the objects in the room and different color schemes are used for color assignment to these objects. The proposed approach is applied to simple as well as complex images from online sources. The proposed approach not only accelerated the process of interior designing but also made it very efficient by giving multiple alternatives.
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