Systems and methods for color and pattern analysis of images of wearable items
Disclosed are methods, systems, and non-transitory computer-readable medium for color and pattern analysis of images including wearable items. For example, a method may include receiving an image depicting a wearable item, identifying the wearable item within the image by identifying a face of an individual wearing the wearable item or segmenting a foreground silhouette of the wearable item from background image portions of the image, determining a portion of the wearable item identified within the image as being a patch portion representative of the wearable item depicted within the image, deriving one or more patterns of the wearable item based on image analysis of the determined patch portion of the image, deriving one or more colors of the wearable item based on image analysis of the determined patch portion of the image, and transmitting information regarding the derived one or more colors and information regarding the derived one or more patterns.
1. A computer-implemented method comprising:
receiving, by one or more processors, an image depicting an item;
identifying, by the one or more processors, the item within the image by identifying a feature of an individual with the item or segmenting a foreground silhouette or aspect of the item from background image portions of the image;
determining, by the one or more processors, a portion of the item identified within the image;
deriving, by the one or more processors, one or more patterns or styles of the item based on image analysis of the determined portion of the image;
deriving, by the one or more processors, one or more colors or characteristics of the item based on image analysis of the determined portion of the image;
determining, by the one or more processors, at least one or more recommended items based on the derived one or more colors or characteristics and/or the derived one or more patterns or styles; and
transmitting, by the one or more processors, information regarding the at least one or more recommended items.
2. The computer-implemented method of claim 1 , wherein determining the portion of the item identified within the image comprises:
locating a portion within the image at a predetermined distance and direction from the identified feature of the individual with the item; and
determining the located portion within the image as the portion of the image.
3. The computer-implemented method of claim 1 , wherein determining the portion of the item identified within the image comprises:
locating a center portion of the segmented foreground silhouette or aspect of the item; and
determining the located center portion as the portion of the image.
4. The computer-implemented method of claim 1 , wherein a size of the portion is based on a size of the identified feature of the individual with the item.
5. The computer-implemented method of claim 1 , wherein deriving the one or more patterns or styles of the item based on image analysis of the determined portion of the image comprises:
performing a fast fourier transform, FFT, of the determined portion of the image; and
deriving at least one of an orientation and a width for each of the one or more patterns or styles based on the FFT of the portion of the image.
6. The computer-implemented method of claim 1 , wherein deriving one or more colors or characteristics of the item based on image analysis of the determined portion of the image comprises:
converting the determined portion of the image from a Red, Green, Blue (RGB) color model to a Hue, Saturation, Lightness (HSL) color model; and
estimating a number of colors or characteristics included in the converted portion of the image.
7. The computer-implemented method of claim 6 , wherein estimating the number of colors or characteristics included in the converted portion of the image comprises:
determining one or more clusters of pixels included in the converted portion of the image, wherein a first cluster and a second cluster are separated by a predetermined distance threshold, and wherein each of the first cluster and the second cluster comprises pixels with a root mean square error (RMSE) smaller than a predetermined RMSE threshold.
8. The computer-implemented method of claim 7 , wherein estimating the number of colors or characteristics included in the converted portion of the image further comprises:
identifying a cluster center for each of the determined one or more clusters of pixels;
classifying the identified cluster center for each of the determined one or more clusters of pixels into categorical values of hue, saturation, and lightness; and
estimating the number of colors or characteristics based on the classified one or more cluster centers.
9. The computer-implemented method of claim 1 , wherein receiving the image depicting the item comprises:
receiving the image depicting the item from one or more of an electronic tenant interface and a user interface.
10. The computer-implemented method of claim 1 , further comprising:
classifying the item within the image based one or more of the information regarding the derived one or more colors or characteristics and the information regarding the derived one or more patterns or styles.
11. A computer system comprising:
a memory having processor-readable instructions stored therein; and
at least one processor configured to access the memory and execute the processor-readable instructions, which when executed by the at least one processor configures the at least one processor to perform a plurality of functions, including functions for:
receiving an image depicting an item;
identifying the item within the image by identifying a feature of an individual with the item or segmenting a foreground silhouette or aspect of the item from background image portions of the image;
determining a portion of the item identified within the image;
deriving one or more patterns or styles of the item based on image analysis of the determined portion of the image;
deriving one or more colors or characteristics of the item based on image analysis of the determined portion of the image;
determining at least one or more recommended items based on the derived one or more colors or characteristics and/or the derived one or more patterns or styles; and
transmitting information regarding the at least one or more recommended items.
12. The computer system of claim 11 , wherein determining the portion of the item identified within the image comprises:
locating a portion within the image at a predetermined distance and direction from the identified feature of the individual with the item; and
determining the located portion within the image as the portion of the image.
13. The computer system of claim 11 , wherein determining the portion of the item identified within the image comprises:
locating a center portion of the segmented foreground silhouette or aspect of the item; and
determining the located center portion as the portion of the image.
14. The computer system of claim 11 , wherein a size of the portion of the image is based on a size of the identified feature of the individual with the item.
15. The computer system of claim 11 , wherein deriving the one or more patterns or styles of the item based on image analysis of the determined portion of the image comprises:
performing a fast fourier transform, FFT, of the determined portion of the image; and
deriving at least one of an orientation and a width for each of the one or more patterns or styles based on the FFT of the portion of the image.
16. The computer system of claim 11 , wherein deriving one or more colors or characteristics of the item based on image analysis of the determined portion of the image comprises:
converting the determined portion of the image from a Red, Green, Blue (RGB) color model to a Hue, Saturation, Lightness (HSL) color model; and
estimating a number of colors or characteristics included in the converted portion of the image.
17. The computer system of claim 16 , wherein estimating the number of colors or characteristics included in the converted portion of the image comprises:
determining one or more clusters of pixels included in the converted portion of the image, wherein a first cluster and a second cluster are separated by a predetermined distance threshold, and wherein each of the first cluster and the second cluster comprises pixels with a root mean square error (RMSE) smaller than a predetermined RMSE threshold.
18. The computer system of claim 17 , wherein estimating the number of colors or characteristics included in the converted portion of the image further comprises:
identifying a cluster center for each of the determined one or more clusters of pixels;
classifying the identified cluster center for each of the determined one or more clusters of pixels into categorical values of hue, saturation, and lightness; and
estimating the number of colors or characteristics based on the classified one or more cluster centers.
19. The computer system of claim 11 , including further functions for:
classifying the item within the image based one or more of the information regarding the derived one or more colors or characteristics and the information regarding the derived one or more patterns or styles.
20. A non-transitory computer-readable medium containing instructions, comprising:
receiving an image depicting an item;
identifying the item within the image by identifying a feature of an individual with the item or segmenting a foreground silhouette or aspect of the item from background image portions of the image;
determining a portion of the item identified within the image;
deriving one or more patterns or styles of the item based on image analysis of the determined portion of the image;
deriving one or more colors or characteristics of the item based on image analysis of the determined portion of the image;
determining at least one or more recommended items based on the derived one or more colors or characteristics and/or the derived one or more patterns or styles; and
transmitting information regarding the at least one or more recommended items.