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.

专业设计中最突出的部分之一是组合正确的颜色。这种组合可能会影响情绪，心理和用户体验，因为组合中的每种颜色都会相互影响。确定颜色的组合非常具有挑战性，因为没有公认的规则。然而，在设计新产品或装饰房间内部时，找到正确的颜色组合至关重要。这项研究的主要动机是即使图像相互重叠，也要从图像中提取显着对象的主色。这样，可以找到特定对象的频繁和流行的颜色组合。因此，首先，设计了一种改进的Inception-ResNet体系结构，以语义方式对图像中的对象进行分割。然后，使用SALGAN来查找图像中的显着对象，因为此处的目的是查找给定图像中显着对象的主色。之后，输出由SALGAN组成结合应用图像和分割图像以获得相应的分割，以在图像上找到显着物体。最后，由于我们旨在量化图像中相应段的像素，因此我们应用了k均值聚类，将样本划分为K个聚类。该算法迭代地工作，以根据其特征将每个数据点分配给K个组之一。数据点根据特征相似性进行聚类。作为聚类的结果，提取了最相关的主色。我们全面的实验调查证明了该方法的有效性。