# US 12,002,085 B2 · Digital image ordering using object position and aesthetics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,002,085 B2 - Document ID: US12002085B2 - Status: Granted - Kind: B2 - Application number: 17/968,636 - Filed: 2022-10-18 - Published: 2023-02-16 - Granted: 2024-06-04 - Earliest claimed: 2019-11-04 - Assignees: Adobe Inc. - Applicants: Adobe Inc. - Inventors: Vikas Kumar; Sourabh Gupta; Nandan Jha; Ajay Bedi - Web page: https://patentgrep.com/patent/US12002085B2 ## CPC - G06Q 30/0643: {graphically representing goods, e.g. 3D product representation} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06F 18/2411: based on the proximity to a decision surface, e.g. support vector machines - G06V 10/255: {Detecting or recognising potential candidate objects based on visual cues, e.g. shapes} - G06V 10/44: Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components - G06V 10/70: using pattern recognition or machine learning (optical pattern recognition or electronic computations therefor G06V10/88) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Digital image ordering based on object position and aesthetics is leveraged in a digital medium environment. According to various implementations, an image analysis system is implemented to identify visual objects in digital images and determine aesthetics attributes of the digital images. The digital images can then be arranged in way that prioritizes digital images that include relevant visual objects and that exhibit optimum visual aesthetics. ## Claims ### Claim 1 A method comprising: identifying, by a computing device, a visual object depicted in a digital image included in a set of digital images; determining, by the computing device, a position of pixels of the visual object in reference to pixels of the digital image; classifying, by the computing device, the visual object as a classified object based on the position; and transforming, by the computing device, the set of digital images into an ordered group of digital images by arranging the set of digital images for display based on the classified object. ### Claim 2 (depends on 1) The method as described in claim 1, wherein the position is determined using coordinates of the pixels of the visual object. ### Claim 3 (depends on 1) The method as described in claim 1, wherein the position is determined using a bitmap mask. ### Claim 4 (depends on 3) The method as described in claim 3, further comprising comparing the bitmap mask to a reference mask. ### Claim 5 (depends on 1) The method as described in claim 1, wherein the position is determined using a boundary box of the visual object and a perimeter of the digital image. ### Claim 6 (depends on 1) The method as described in claim 1, wherein the visual object is identified using a machine learning model trained on data to identify objects depicted in digital images. ### Claim 7 (depends on 1) The method as described in claim 1, further comprising identifying two digital images from the set of digital images that are similar. ### Claim 8 (depends on 7) The method as described in claim 7, wherein one of the two digital images is omitted from the ordered group of digital images. ### Claim 9 (depends on 1) The method as described in claim 1, further comprising generating an aesthetics score for the digital image. ### Claim 10 (depends on 9) The method as described in claim 9, wherein the digital image is included in the ordered group of digital images based on the aesthetics score. ### Claim 11 (depends on 9) The method as described in claim 9, wherein the digital image is omitted from the ordered group of digital images based on the aesthetics score. ### Claim 12 A system comprising: a memory component; and a processing device coupled to the memory component, the processing device to perform operations comprising: identifying a visual object depicted in a digital image included in a set of digital images; determining a position of pixels of the visual object in reference to pixels of the digital image; classifying the visual object as a classified object based on the position; and transforming the set of digital images into an ordered group of digital images by rearranging an order of the set of digital images for display based on the classified object. ### Claim 13 (depends on 12) The system as described in claim 12, wherein the position is determined using coordinates of the pixels of the visual object. ### Claim 14 (depends on 12) The system as described in claim 12, wherein the position is determined using a bitmap mask. ### Claim 15 (depends on 12) The system as described in claim 12, wherein the position is determined using a boundary box of the visual object and a perimeter of the digital image. ### Claim 16 A system comprising: means for identifying a visual object depicted in digital images included in a set of digital images having an order; means for determining a position of pixels of the visual object in reference to pixels of each of the digital images; means for classifying the visual object as a classified object; and means for transforming the set of digital images into an ordered group of digital images by rearranging the order of the set of digital images for display based on the classified object. ### Claim 17 (depends on 16) The system as described in claim 16, wherein the visual object is identified using a machine learning model trained on data to identify objects depicted in digital images. ### Claim 18 (depends on 16) The system as described in claim 16, wherein the position is determined using a boundary box of the visual object. ### Claim 19 (depends on 16) The system as described in claim 16, wherein the position is determined using coordinates of the pixels of the visual object. ### Claim 20 (depends on 16) The system as described in claim 16, wherein the position is determined using a bitmap mask and a reference mask.