A better way of extracting dominant colors using salient objects with semantic segmentation

Gündüz A. B., Taşkın B., Yavuz A. G., Karslıgil Yavuz M. E.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, sa.100, ss.1-15, 2021 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2021
  • Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Computer & Applied Sciences, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-15
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

One of the most prominent parts of professional design consists of combining the right colors. This combination can affect emotions, psychology, and user experience since each color in the combination has a unique effect on each other. It is a very challenging to determine the combination of colors since there are no universally accepted rules for it. Yet finding the right color combination is crucial when it comes to designing a new product or decorating the interiors of a room. The main motivation of this study is to extract the dominant colors of a salient object from an image even if the objects overlap each other. In this way, it is possible to find frequent and popular color combinations of a specific object. So, first of all, a modified Inception-ResNet architecture was designed semantically segmentate objects in the image. Then, SALGAN was applied to find the salient object in the image since the aim here is to find the dominant colors of the salient object in a given image. After that, the outputs consisted of the SALGAN applied image and segmented image were combined to obtain the corresponding segment for the purpose of finding the salient object on the image. Finally, since we aimed to quantize the pixels of the corresponding segment in the image, we applied k-means clustering which partitions samples into K clusters. The algorithm works iteratively to assign each data point to one of the K groups based on their features. Data points were clustered according to feature similarity. As a result the clustering, the most relevant dominant colors were extracted. Our comprehensive experimental survey has demonstrated the effectiveness of the proposed method.