Building upon the key discovery that color pairs play an important role in harmony estimation, in this paper we present a novel color‐pair based estimation model to accurately measure the color harmony.
COLOR PALETTE FROM IMAGE TINEYE PROFESSIONAL
However, a widely open research question is: how can we establish a robust and accurate color harmony measure for the public and professional designers to identify the harmony level of a color theme or color set. Harmonious color combinations can stimulate positive user emotional responses. The algorithm proposed in this paper is tested on three data sets: Corel-1k, Corel-5k and Corel-10k, and the experimental results show that it is superior to the current content-based image retrieval methods. Finally, the combination of the dominant dolor descriptor and the Hu moments is used for content-based image retrieval.
Besides, the translation and rotation invariance of the Hu moments feature is applied to extract the shape information in the same consistent zone of the image. Therefore, the proposed method in this paper first applies the texton template to detect and extract the consistent zone of an image, and calculates the dominant color descriptor feature on the pixels in this consistent zone. The human eye is usually more sensitive to zones of consistent color, often identifying objects by zones of consistency. Due to the influence of lighting and other factors, the same color in nature may have some different changes. Meanwhile, the dominant color descriptor has been widely used in image processing. The rapidly increasing number of digital images requires effective retrieval. This is accomplished by comparing the city colours obtained by executing the K-Means clustering technique on these 12 cities and comparing these colours with those that were obtained from the previous FCM study The goal of this current research is to determine if the type of clustering method implemented would impact the results of the extracted colours. It applies K-Means clustering to acquire five dominant colours for each of these 12 cities. This current research is a follow-on investigation of the previous city colour extraction study. These cities were Cairo, Cape Town, Singapore, Tokyo, Perth, Sydney, London, Madrid, Mexico City, New York City, Buenos Aires, and Lima. An earlier investigation implemented Fuzzy C-Means (FCM) colour extraction to identify five dominant colours from the images of each of the twelve major cosmopolitan cities that are situated on six continents. For urban development and urban renewal projects, incorporating a city’s existing colour palette into a construction plan would ensure that the completed project would be in harmony with the existing colour schemes of a neighbourhood.
The colour palette of each urban metropolis reflects its cultural identity and its unique flair. Our method can extract prominent colors from small image regions as the dominant colors, which is not possible using conventional methods.
Next, the algorithm calculates the color features such as the saturation, contrast, and area of each cluster, based on which it extracts the dominant colors. The proposed method first calculates the initial dominant color candidates using the K-means algorithm in the CIELAB color space and the graph cut of a region adjacency graph (RAG) of the segmented image. This study developed an approach to automatically extract dominant colors based on color features that are typically considered by human observers when analyzing color schemes. However, these methods cannot extract the dominant colors of small regions, which are essential for the analysis of color schemes. Conventionally, dominant colors are extracted using clustering or histogram-based methods. The dominant colors in an image can be used for image search, color editing, palette generation, and several other applications.
0 kommentar(er)
