Patent Publication Number: US-11048975-B2

Title: Systems and methods for identifying items in a digital image

Description:
TECHNICAL FIELD 
     This disclosure relates generally to machine vision systems, and more particularly related to systems and methods for identifying items displayed in images comprising multiple items. 
     BACKGROUND 
     As the progress of artificial intelligence technologies continues to move forward, the need for accurate machine vision systems is becoming more apparent. Development of these machine vision systems, though, suffers from a number of problems. Specifically, training the machine learning algorithms used to operate these systems is not only computationally intense, but also expensive from a budgetary perspective. These expenses are due to the fact that in the past, training data sets for the machine learning algorithms that operate machine vision systems had to be developed de novo. For example, large collections of images used to train machine learning algorithms had to be either manually labeled prior to training the machine learning algorithm or, in automated systems, computationally intensive labeling algorithms need to be used. Therefore, there is a need for bootstrapped systems and methods that reduce the computational intensity of automated labeling systems for creating machine learning algorithms that operate machine vision systems. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       To facilitate further description of the embodiments, the following drawings are provided in which: 
         FIG. 1  illustrates a front elevational view of a computer system that is suitable for implementing various embodiments of the systems disclosed in  FIG. 3 ; 
         FIG. 2  illustrates a representative block diagram of an example of the elements included in the circuit boards inside a chassis of the computer system of  FIG. 1 ; 
         FIG. 3  illustrates a representative block diagram of a system, according to an embodiment; 
         FIG. 4  is a flowchart for a method, according to certain embodiments; 
         FIG. 5  is a flowchart for a method, according to certain embodiments; 
         FIG. 6  is a flowchart for a method, according to certain embodiments; 
         FIG. 7  illustrates a representative block diagram of a system, according to an embodiment; 
         FIG. 8  illustrates a representative block diagram of a system, according to an embodiment; and 
         FIG. 9  illustrates a representative block diagram of a system, according to an embodiment. 
     
    
    
     For simplicity and clarity of illustration, the drawing figures illustrate the general manner of construction, and descriptions and details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the present disclosure. Additionally, elements in the drawing figures are not necessarily drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help improve understanding of embodiments of the present disclosure. The same reference numerals in different figures denote the same elements. 
     The terms “first,” “second,” “third,” “fourth,” and the like in the description and in the claims, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms “include,” and “have,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, device, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, system, article, device, or apparatus. 
     The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,” “under,” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the apparatus, methods, and/or articles of manufacture described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein. 
     The terms “couple,” “coupled,” “couples,” “coupling,” and the like should be broadly understood and refer to connecting two or more elements mechanically and/or otherwise. Two or more electrical elements may be electrically coupled together, but not be mechanically or otherwise coupled together. Coupling may be for any length of time, e.g., permanent or semi-permanent or only for an instant. “Electrical coupling” and the like should be broadly understood and include electrical coupling of all types. The absence of the word “removably,” “removable,” and the like near the word “coupled,” and the like does not mean that the coupling, etc. in question is or is not removable. 
     As defined herein, two or more elements are “integral” if they are comprised of the same piece of material. As defined herein, two or more elements are “non-integral” if each is comprised of a different piece of material. 
     As defined herein, “real-time” can, in some embodiments, be defined with respect to operations carried out as soon as practically possible upon occurrence of a triggering event. A triggering event can include receipt of data necessary to execute a task or to otherwise process information. Because of delays inherent in transmission and/or in computing speeds, the term “real time” encompasses operations that occur in “near” real time or somewhat delayed from a triggering event. In a number of embodiments, “real time” can mean real time less a time delay for processing (e.g., determining) and/or transmitting data. The particular time delay can vary depending on the type and/or amount of the data, the processing speeds of the hardware, the transmission capability of the communication hardware, the transmission distance, etc. However, in many embodiments, the time delay can be less than approximately one second, two seconds, five seconds, or ten seconds. 
     As defined herein, “approximately” can, in some embodiments, mean within plus or minus ten percent of the stated value. In other embodiments, “approximately” can mean within plus or minus five percent of the stated value. In further embodiments, “approximately” can mean within plus or minus three percent of the stated value. In yet other embodiments, “approximately” can mean within plus or minus one percent of the stated value. 
     DESCRIPTION OF EXAMPLES OF EMBODIMENTS 
     A number of embodiments can include a system. The system can include one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors. The computing instructions can be configured to run on the one or more processors and perform acts of receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices. 
     Various embodiments include a method. The method can include receiving one or more digital images from a repository of digital images; annotating the one or more digital images from the repository of digital images; digitally altering the one or more digital images, as annotated, from the repository of digital images; digitally combining the one or more digital images, as annotated and digitally altered, with at least one or more portions of one or more other digital images of the repository of digital images to create one or more combined digital images; training a machine learning algorithm on the one or more combined digital images; and storing the machine learning algorithm, as trained, in the one or more non-transitory computer readable storage devices. 
     A number of embodiments can include a system. The system can include one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors. The computing instructions can be configured to run on the one or more processors and perform acts of receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image. 
     Various embodiments include a method. The method can include receiving a digital image comprising multiple items; determining an embedding for the digital image using a machine learning algorithm trained on one or more combined digital images, the combined digital image comprising one or more annotated digital images; identifying an item of the multiple items in the digital image; and facilitating an alteration of a GUI on an electronic device in response to identifying the item in the digital image. 
     Turning to the drawings,  FIG. 1  illustrates an exemplary embodiment of a computer system  100 , all of which or a portion of which can be suitable for (i) implementing part or all of one or more embodiments of the techniques, methods, and systems and/or (ii) implementing and/or operating part or all of one or more embodiments of the memory storage modules described herein. As an example, a different or separate one of a chassis  102  (and its internal components) can be suitable for implementing part or all of one or more embodiments of the techniques, methods, and/or systems described herein. Furthermore, one or more elements of computer system  100  (e.g., a monitor  106 , a keyboard  104 , and/or a mouse  110 , etc.) also can be appropriate for implementing part or all of one or more embodiments of the techniques, methods, and/or systems described herein. Computer system  100  can comprise chassis  102  containing one or more circuit boards (not shown), a Universal Serial Bus (USB) port  112 , a Compact Disc Read-Only Memory (CD-ROM) and/or Digital Video Disc (DVD) drive  116 , and a hard drive  114 . A representative block diagram of the elements included on the circuit boards inside chassis  102  is shown in  FIG. 2 . A central processing unit (CPU)  210  in  FIG. 2  is coupled to a system bus  214  in  FIG. 2 . In various embodiments, the architecture of CPU  210  can be compliant with any of a variety of commercially distributed architecture families. 
     Continuing with  FIG. 2 , system bus  214  also is coupled to a memory storage unit  208 , where memory storage unit  208  can comprise (i) non-volatile memory, such as, for example, read only memory (ROM) and/or (ii) volatile memory, such as, for example, random access memory (RAM). The non-volatile memory can be removable and/or non-removable non-volatile memory. Meanwhile, RAM can include dynamic RAM (DRAM), static RAM (SRAM), etc. Further, ROM can include mask-programmed ROM, programmable ROM (PROM), one-time programmable ROM (OTP), erasable programmable read-only memory (EPROM), electrically erasable programmable ROM (EEPROM) (e.g., electrically alterable ROM (EAROM) and/or flash memory), etc. In these or other embodiments, memory storage unit  208  can comprise (i) non-transitory memory and/or (ii) transitory memory. 
     In various examples, portions of the memory storage module(s) of the various embodiments disclosed herein (e.g., portions of the non-volatile memory storage module(s)) can be encoded with a boot code sequence suitable for restoring computer system  100  ( FIG. 1 ) to a functional state after a system reset. In addition, portions of the memory storage module(s) of the various embodiments disclosed herein (e.g., portions of the non-volatile memory storage module(s)) can comprise microcode such as a Basic Input-Output System (BIOS) operable with computer system  100  ( FIG. 1 ). In the same or different examples, portions of the memory storage module(s) of the various embodiments disclosed herein (e.g., portions of the non-volatile memory storage module(s)) can comprise an operating system, which can be a software program that manages the hardware and software resources of a computer and/or a computer network. The BIOS can initialize and test components of computer system  100  ( FIG. 1 ) and load the operating system. Meanwhile, the operating system can perform basic tasks such as, for example, controlling and allocating memory, prioritizing the processing of instructions, controlling input and output devices, facilitating networking, and managing files. Exemplary operating systems can comprise one of the following: (i) Microsoft® Windows® operating system (OS) by Microsoft Corp. of Redmond, Wash., United States of America, (ii) Mac® OS X by Apple Inc. of Cupertino, Calif., United States of America, (iii) UNIX® OS, and (iv) Linux® OS. Further exemplary operating systems can comprise one of the following: (i) the iOS® operating system by Apple Inc. of Cupertino, Calif., United States of America, (ii) the Blackberry® operating system by Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) the WebOS operating system by LG Electronics of Seoul, South Korea, (iv) the Android™ operating system developed by Google, of Mountain View, Calif., United States of America, (v) the Windows Mobile™ operating system by Microsoft Corp. of Redmond, Wash., United States of America, or (vi) the Symbian™ operating system by Accenture PLC of Dublin, Ireland. 
     As used herein, “processor” and/or “processing module” means any type of computational circuit, such as but not limited to a microprocessor, a microcontroller, a controller, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor, or any other type of processor or processing circuit capable of performing the desired functions. In some examples, the one or more processing modules of the various embodiments disclosed herein can comprise CPU  210 . 
     Alternatively, or in addition to, the systems and procedures described herein can be implemented in hardware, or a combination of hardware, software, and/or firmware. For example, one or more application specific integrated circuits (ASICs) can be programmed to carry out one or more of the systems and procedures described herein. For example, one or more of the programs and/or executable program components described herein can be implemented in one or more ASICs. In many embodiments, an application specific integrated circuit (ASIC) can comprise one or more processors or microprocessors and/or memory blocks or memory storage. 
     In the depicted embodiment of  FIG. 2 , various I/O devices such as a disk controller  204 , a graphics adapter  224 , a video controller  202 , a keyboard adapter  226 , a mouse adapter  206 , a network adapter  220 , and other I/O devices  222  can be coupled to system bus  214 . Keyboard adapter  226  and mouse adapter  206  are coupled to keyboard  104  ( FIGS. 1-2 ) and mouse  110  ( FIGS. 1-2 ), respectively, of computer system  100  ( FIG. 1 ). While graphics adapter  224  and video controller  202  are indicated as distinct units in  FIG. 2 , video controller  202  can be integrated into graphics adapter  224 , or vice versa in other embodiments. Video controller  202  is suitable for monitor  106  ( FIGS. 1-2 ) to display images on a screen  108  ( FIG. 1 ) of computer system  100  ( FIG. 1 ). Disk controller  204  can control hard drive  114  ( FIGS. 1-2 ), USB port  112  ( FIGS. 1-2 ), and CD-ROM drive  116  ( FIGS. 1-2 ). In other embodiments, distinct units can be used to control each of these devices separately. 
     Network adapter  220  can be suitable to connect computer system  100  ( FIG. 1 ) to a computer network by wired communication (e.g., a wired network adapter) and/or wireless communication (e.g., a wireless network adapter). In some embodiments, network adapter  220  can be plugged or coupled to an expansion port (not shown) in computer system  100  ( FIG. 1 ). In other embodiments, network adapter  220  can be built into computer system  100  ( FIG. 1 ). For example, network adapter  220  can be built into computer system  100  ( FIG. 1 ) by being integrated into the motherboard chipset (not shown), or implemented via one or more dedicated communication chips (not shown), connected through a PCI (peripheral component interconnector) or a PCI express bus of computer system  100  ( FIG. 1 ) or USB port  112  ( FIG. 1 ). 
     Returning now to  FIG. 1 , although many other components of computer system  100  are not shown, such components and their interconnection are well known to those of ordinary skill in the art. Accordingly, further details concerning the construction and composition of computer system  100  and the circuit boards inside chassis  102  are not discussed herein. 
     Meanwhile, when computer system  100  is running, program instructions (e.g., computer instructions) stored on one or more of the memory storage module(s) of the various embodiments disclosed herein can be executed by CPU  210  ( FIG. 2 ). At least a portion of the program instructions, stored on these devices, can be suitable for carrying out at least part of the techniques and methods described herein. 
     Further, although computer system  100  is illustrated as a desktop computer in  FIG. 1 , there can be examples where computer system  100  may take a different form factor while still having functional elements similar to those described for computer system  100 . In some embodiments, computer system  100  may comprise a single computer, a single server, or a cluster or collection of computers or servers, or a cloud of computers or servers. Typically, a cluster or collection of servers can be used when the demand on computer system  100  exceeds the reasonable capability of a single server or computer. In certain embodiments, computer system  100  may comprise a portable computer, such as a laptop computer. In certain other embodiments, computer system  100  may comprise a mobile electronic device, such as a smartphone. In certain additional embodiments, computer system  100  may comprise an embedded system. 
     Turning ahead in the drawings,  FIG. 3  illustrates a block diagram of a system  300  that can be employed for identifying items in a digital image, as described in greater detail below. System  300  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  300  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  300  can perform various procedures, processes, and/or activities. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  300 . 
     Generally, therefore, system  300  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  300  described herein. 
     In some embodiments, system  300  can include a web server  310  and/or an Internet  320 . Web server  310  and/or internet  320  can each be a computer system, such as computer system  100  ( FIG. 1 ), as described above, and can each be a single computer, a single server, or a cluster or collection of computers or servers, or a cloud of computers or servers. In another embodiment, a single computer system can host each of two or more of web server  310  and/or internet  320 . Additional details regarding web server  310  and/or user internet  320  are described herein. 
     In many embodiments, system  300  also can comprise user computers  330 ,  331 . User computers  330 ,  331  can comprise any of the elements described in relation to computer system  100 . In some embodiments, user computers  330 ,  331  can be mobile devices. A mobile electronic device can refer to a portable electronic device (e.g., an electronic device easily conveyable by hand by a person of average size) with the capability to present audio and/or visual data (e.g., text, images, videos, music, etc.). For example, a mobile electronic device can comprise at least one of a digital media player, a cellular telephone (e.g., a smartphone), a personal digital assistant, a handheld digital computer device (e.g., a tablet personal computer device), a laptop computer device (e.g., a notebook computer device, a netbook computer device), a wearable user computer device, or another portable computer device with the capability to present audio and/or visual data (e.g., images, videos, music, etc.). Thus, in many examples, a mobile electronic device can comprise a volume and/or weight sufficiently small as to permit the mobile electronic device to be easily conveyable by hand. For examples, in some embodiments, a mobile electronic device can occupy a volume of less than or equal to approximately 1790 cubic centimeters, 2434 cubic centimeters, 2876 cubic centimeters, 4056 cubic centimeters, and/or 5752 cubic centimeters. Further, in these embodiments, a mobile electronic device can weigh less than or equal to 15.6 Newtons, 17.8 Newtons, 22.3 Newtons, 31.2 Newtons, and/or 44.5 Newtons. 
     Exemplary mobile electronic devices can comprise (i) an iPod®, iPhone®, iTouch®, iPad®, MacBook® or similar product by Apple Inc. of Cupertino, Calif., United States of America, (ii) a Blackberry® or similar product by Research in Motion (RIM) of Waterloo, Ontario, Canada, (iii) a Lumia® or similar product by the Nokia Corporation of Keilaniemi, Espoo, Finland, and/or (iv) a Galaxy™ or similar product by the Samsung Group of Samsung Town, Seoul, South Korea. Further, in the same or different embodiments, a mobile electronic device can comprise an electronic device configured to implement one or more of (i) the iPhone® operating system by Apple Inc. of Cupertino, Calif., United States of America, (ii) the Blackberry® operating system by Research In Motion (RIM) of Waterloo, Ontario, Canada, (iii) the Palm® operating system by Palm, Inc. of Sunnyvale, Calif., United States, (iv) the Android™ operating system developed by the Open Handset Alliance, (v) the Windows Mobile™ operating system by Microsoft Corp. of Redmond, Wash., United States of America, or (vi) the Symbian™ operating system by Nokia Corp. of Keilaniemi, Espoo, Finland. 
     Further still, the term “wearable user computer device” as used herein can refer to an electronic device with the capability to present audio and/or visual data (e.g., text, images, videos, music, etc.) that is configured to be worn by a user and/or mountable (e.g., fixed) on the user of the wearable user computer device (e.g., sometimes under or over clothing; and/or sometimes integrated with and/or as clothing and/or another accessory, such as, for example, a hat, eyeglasses, a wrist watch, shoes, etc.). In many examples, a wearable user computer device can comprise a mobile electronic device, and vice versa. However, a wearable user computer device does not necessarily comprise a mobile electronic device, and vice versa. 
     In specific examples, a wearable user computer device can comprise a head mountable wearable user computer device (e.g., one or more head mountable displays, one or more eyeglasses, one or more contact lenses, one or more retinal displays, etc.) or a limb mountable wearable user computer device (e.g., a smart watch). In these examples, a head mountable wearable user computer device can be mountable in close proximity to one or both eyes of a user of the head mountable wearable user computer device and/or vectored in alignment with a field of view of the user. 
     In more specific examples, a head mountable wearable user computer device can comprise (i) Google Glass™ product or a similar product by Google Inc. of Menlo Park, Calif., United States of America; (ii) the Eye Tap™ product, the Laser Eye Tap™ product, or a similar product by ePI Lab of Toronto, Ontario, Canada, and/or (iii) the Raptyr™ product, the STAR 1200™ product, the Vuzix Smart Glasses M100™ product, or a similar product by Vuzix Corporation of Rochester, N.Y., United States of America. In other specific examples, a head mountable wearable user computer device can comprise the Virtual Retinal Display™ product, or similar product by the University of Washington of Seattle, Wash., United States of America. Meanwhile, in further specific examples, a limb mountable wearable user computer device can comprise the iWatch™ product, or similar product by Apple Inc. of Cupertino, Calif., United States of America, the Galaxy Gear or similar product of Samsung Group of Samsung Town, Seoul, South Korea, the Moto 360 product or similar product of Motorola of Schaumburg, Ill., United States of America, and/or the Zip™ product, One™ product, Flex™ product, Charge™ product, Surge™ product, or similar product by Fitbit Inc. of San Francisco, Calif., United States of America. 
     In some embodiments, web server  310  can be in data communication through Internet  320  with user computers  330 ,  331 . In certain embodiments, user computers  330 ,  331  can be desktop computers, laptop computers, smart phones, tablet devices, and/or other endpoint devices. Web server  310  can host one or more websites. For example, web server  310  can host an eCommerce website that allows users to browse and/or search for products, to add products to an electronic shopping cart, and/or to purchase products, in addition to other suitable activities. 
     In many embodiments, web server  310 , Internet  320 , and/or user computers  330 ,  331  can each comprise one or more input devices (e.g., one or more keyboards, one or more keypads, one or more pointing devices such as a computer mouse or computer mice, one or more touchscreen displays, a microphone, etc.), and/or can each comprise one or more display devices (e.g., one or more monitors, one or more touch screen displays, projectors, etc.). In these or other embodiments, one or more of the input device(s) can be similar or identical to keyboard  104  ( FIG. 1 ) and/or a mouse  110  ( FIG. 1 ). Further, one or more of the display device(s) can be similar or identical to monitor  106  ( FIG. 1 ) and/or screen  108  ( FIG. 1 ). The input device(s) and the display device(s) can be coupled to the processing module(s) and/or the memory storage module(s) web server  310 , Internet  320 , and/or user computers  330 ,  331  in a wired manner and/or a wireless manner, and the coupling can be direct and/or indirect, as well as locally and/or remotely. As an example of an indirect manner (which may or may not also be a remote manner), a keyboard-video-mouse (KVM) switch can be used to couple the input device(s) and the display device(s) to the processing module(s) and/or the memory storage module(s). In some embodiments, the KVM switch also can be part of web server  310 , Internet  320 , and/or user computers  330 ,  331 . In a similar manner, the processing module(s) and the memory storage module(s) can be local and/or remote to each other. 
     In many embodiments, web server  310  can be configured to communicate with one or more user computers  330 ,  331 . In some embodiments, user computers  330 ,  331  also can be referred to as customer computers. In some embodiments, web server  310  can communicate or interface (e.g., interact) with one or more customer computers (such as user computers  330 ,  331 ) through a network or Internet  320 , which can be a public or private network. Internet  320  can be an intranet that is not open to the public. Accordingly, in many embodiments, web server  310  (and/or the software used by such systems) can refer to a back end of system  300  operated by an operator and/or administrator of system  300 , and user computers  330 ,  331  (and/or the software used by such systems) can refer to a front end of system  300  used by one or more users  330 ,  331 , respectively. In some embodiments, users  330 ,  331  also can be referred to as customers, in which case, user computers  330 ,  331  can be referred to as customer computers. In these or other embodiments, the operator and/or administrator of system  300  can manage system  300 , the processing module(s) of system  300 , and/or the memory storage module(s) of system  300  using the input device(s) and/or display device(s) of system  300 . 
     Meanwhile, in many embodiments, web server  310  and/or user computers  330 ,  340  also can be configured to communicate with one or more databases. The one or more databases can comprise a product database that contains information about products, items, or SKUs (stock keeping units) sold by a retailer. The one or more databases can be stored on one or more memory storage modules (e.g., non-transitory memory storage module(s)), which can be similar or identical to the one or more memory storage module(s) (e.g., non-transitory memory storage module(s)) described above with respect to computer system  100  ( FIG. 1 ). Also, in some embodiments, for any particular database of the one or more databases, that particular database can be stored on a single memory storage module of the memory storage module(s), and/or the non-transitory memory storage module(s) storing the one or more databases or the contents of that particular database can be spread across multiple ones of the memory storage module(s) and/or non-transitory memory storage module(s) storing the one or more databases, depending on the size of the particular database and/or the storage capacity of the memory storage module(s) and/or non-transitory memory storage module(s). 
     The one or more databases can each comprise a structured (e.g., indexed) collection of data and can be managed by any suitable database management systems configured to define, create, query, organize, update, and manage database(s). Exemplary database management systems can include MySQL (Structured Query Language) Database, PostgreSQL Database, Microsoft SQL Server Database, Oracle Database, SAP (Systems, Applications, &amp; Products) Database, IBM DB2 Database, and/or NoSQL Database. 
     Meanwhile, communication between web server  310 , internet  320 , user computers  330 ,  331 , and/or the one or more databases can be implemented using any suitable manner of wired and/or wireless communication. Accordingly, system  300  can comprise any software and/or hardware components configured to implement the wired and/or wireless communication. Further, the wired and/or wireless communication can be implemented using any one or any combination of wired and/or wireless communication network topologies (e.g., ring, line, tree, bus, mesh, star, daisy chain, hybrid, etc.) and/or protocols (e.g., personal area network (PAN) protocol(s), local area network (LAN) protocol(s), wide area network (WAN) protocol(s), cellular network protocol(s), powerline network protocol(s), etc.). Exemplary PAN protocol(s) can comprise Bluetooth, Zigbee, Wireless Universal Serial Bus (USB), Z-Wave, etc.; exemplary LAN and/or WAN protocol(s) can comprise Institute of Electrical and Electronic Engineers (IEEE) 802.3 (also known as Ethernet), IEEE 802.11 (also known as WiFi), etc.; and exemplary wireless cellular network protocol(s) can comprise Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Evolution-Data Optimized (EV-DO), Enhanced Data Rates for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), Digital Enhanced Cordless Telecommunications (DECT), Digital AMPS (IS-136/Time Division Multiple Access (TDMA)), Integrated Digital Enhanced Network (iDEN), Evolved High-Speed Packet Access (HSPA+), Long-Term Evolution (LTE), WiMAX, etc. The specific communication software and/or hardware implemented can depend on the network topologies and/or protocols implemented, and vice versa. In many embodiments, exemplary communication hardware can comprise wired communication hardware including, for example, one or more data buses, such as, for example, universal serial bus(es), one or more networking cables, such as, for example, coaxial cable(s), optical fiber cable(s), and/or twisted pair cable(s), any other suitable data cable, etc. Further exemplary communication hardware can comprise wireless communication hardware including, for example, one or more radio transceivers, one or more infrared transceivers, etc. Additional exemplary communication hardware can comprise one or more networking components (e.g., modulator-demodulator components, gateway components, etc.). 
     In many embodiments, system  300  can comprise graphical user interface (“GUI”)  340 ,  341 . In the same or different embodiments, GUI  340 ,  341  can be part of and/or displayed by user computers  330 ,  331 , which also can be part of system  300 . In some embodiments, GUI  340 ,  341  can comprise text and/or graphics (image) based user interfaces. In the same or different embodiments, GUI  330 ,  331  can comprise a heads up display (“HUD”). When GUI  340 ,  341  comprises a HUD, GUI  340 ,  341  can be projected onto glass or plastic, displayed in midair as a hologram, or displayed on monitor  106  ( FIG. 1 ). In various embodiments, GUI  340 ,  341  can be color or black and white. In many embodiments, GUI  340 ,  341  can comprise an application running on a computer system, such as computer system  100 , user computers  330 ,  331 , and/or web server  310 . In the same or different embodiments, GUI  340 ,  341  can be generated by user computers  340 ,  341 . In many embodiments, web server  310  can facilitate a display of and/or transmit instructions to display GUI  340 ,  341 . In various embodiments, GUI  340 ,  341  can be displayed on user computers  330 ,  331  prior to an initiation of methods described herein (e.g. methods  400  ( FIG. 4 ),  500  ( FIG. 5 ), and/or  600  ( FIG. 6 )). In the same or different embodiments, GUI  340 ,  341  can comprise a website accessed through internet  320 . In some embodiments, GUI  340 ,  341  can comprise an eCommerce website. In the same or different embodiments, GUI  340 ,  341  can be displayed as or on a virtual reality (VR) and/or augmented reality (AR) system or display. 
     As described above, development of machine vision systems suffers from a number of problems surrounding the labor and computational intensity of developing training data sets for training machine learning algorithms that operate machine vision systems. As described in further detail below, these problems can be solved by bootstrapping pre-existing libraries of images to train machine vision systems. For example, libraries of stock images can be used. Stock images are particularly applicable to this problem because they can comprise a pre-existing repository of digital images that are pre-labeled with an identity of an item shown in the digital image. This identity label can then be combined with other labels for the item (e.g., labels associated with an item taxonomy) to create a label rich training dataset for a machine learning algorithm without the labor and computationally intensive steps of generating a training data set de novo. In this way, the systems and methods described herein can reduce the computational loads on systems used to generate these training data sets, decrease processing times associated with generating these training data sets, and increase the rate at which trained machine learning algorithms can be implemented in machine vision systems. 
     Turning ahead in the drawings,  FIG. 4  illustrates a flow chart for a method  400 , according to an embodiment. Method  400  is merely exemplary and is not limited to the embodiments presented herein. Method  400  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the activities of method  400  can be performed in the order presented. In other embodiments, the activities of method  400  can be performed in any suitable order. In still other embodiments, one or more of the activities of method  400  can be combined or skipped. In many embodiments, system  300  ( FIG. 3 ) can be suitable to perform method  400  and/or one or more of the activities of method  400 . In these or other embodiments, one or more of the activities of method  400  can be implemented as one or more computer instructions configured to run at one or more processing modules and configured to be stored at one or more non-transitory memory storage modules. Such non-transitory memory storage modules can be part of a computer system such as web server  310  and/or user computers  330 ,  331 . The processing module(s) can be similar or identical to the processing module(s) described above with respect to computer system  100  ( FIG. 1 ). 
     In many embodiments, method  400  can comprise an activity  401  of receiving one or more digital images from a repository of digital images. In some embodiments, activity  401  can include optional activities  402  and/or  403 . In the same or different embodiments, a repository of digital images can comprise stock images and/or lifestyle images. In the same or different embodiments, a digital image can comprise a stock image or a lifestyle image. In some embodiments, a stock image can comprise an image of only one item. In various embodiments, an item can have multiple stock images. In embodiments where the item can have multiple stock images, each stock image can be photographed from a different angle (e.g. front view of the item, isometric view of the item, side view of the item, top view of the item, bottom view of the item, etc.). In the same or different embodiments, a stock image can comprise a plain background. In many embodiments, a plain background can comprise a single color and/or a pattern recognizable by a machine learning algorithm. In some embodiments, a lifestyle image can comprise an image of multiple items. In the same or different embodiments, items in a stock image can have a unifying theme and/or style (e.g., be the same color, complementary colors, designed using the same design principles, inspired by the same era, be made of the same materials, have the same patterns, have similar patterns, have complementary patterns, etc.) In various embodiments, a lifestyle image can comprise an image of a room comprising one or more pieces of furniture (e.g. a picture of a living room), one or more appliances (e.g. a picture of a kitchen or laundry room), one or more pieces of clothering (e.g. when an embodiment is used for wardrobe recommendation), and/or one or more landscapes (e.g. plants, outdoor furniture, playground structures, etc.). In the same or different embodiments, a lifestyle image can be created by a computer system (e.g. as described below in activities  409 - 411 ) and/or be an image taken using a camera. 
     In some embodiments, stock images and/or lifestyle images can comprise one or more tags identifying items within the image. In the same or different embodiments, one or more tags can comprise levels of an item taxonomy. In many embodiments, an item taxonomy can be configured to classify a catalogue of items based on properties of each item of the catalogue of items. In the same or different embodiments, properties of an item can comprise a title, a description, a price, a brand, a manufacturer, a color, a quantity, a volume, a weight, a material, a style, a pattern, a theme, a recommended use, a color, a fabric, etc. In some embodiments, an item taxonomy can comprise distinct levels of item classification. In further embodiments, distinct levels of item classification can narrow as the distinct levels go deeper into an item taxonomy. In various embodiments, distinct levels of item classification can comprise a super department, a department, a category, and/or a sub-category. In many embodiments, a department can be deeper in an item taxonomy than a super department. In the same or different embodiments, a category can be deeper in an item taxonomy than a department. In some embodiments, a sub-category can be deeper in an item taxonomy than a category. For example, an item taxonomy for Shamrock Farms whole milk can comprise a super department of “Eggs and Dairy,” a department of “Milk,” a category of “Dairy Milk,” and a sub-category of “Whole Milk.” As another non-limiting example, an item taxonomy for a sofa can comprise a super department of “Home,” a department of “Furniture and Appliances,” a category of “Living Room,” and a sub-category of “Sofas and Sectionals .” In both examples described above, the item taxonomy can be further segmented into brand/manufacturer if needed. In many embodiments, tags of an item can be determined based upon attributes of an item (e.g., properties of an item as described above). In the same or different embodiments, tags of an item can be provided by a seller of an item and/or by a user of an eCommerce website. In many embodiments, user reviews and/or product descriptions can be analyzed by a machine learning algorithm trained to process natural language, and tags of an item can be extracted by the machine learning algorithm. In various embodiments, tags of an item can comprise functional features of an item (e.g., sturdy support, rustic wood, cushion back, upholstered, floral, split sofa, contemporary sofa, residential sofa, rounded legs, etc.). 
     In many embodiments, method  400  can comprise an activity  402  of identifying a lifestyle image. In some embodiments, a lifestyle image can be identified using a deep learning algorithm trained to determine whether an image is a lifestyle image and/or a stock image. In the same or different embodiments, a deep learning algorithm can be trained on a randomly sampled set of lifestyle images and/or stock images. In some embodiments, a training dataset can be tagged via crowdsourcing. In the same or different embodiments, a convolutional neural network can be used to identify a lifestyle image. In many embodiments, a base network of a deep learning algorithm can comprise a VGG16 algorithm. In the same or different embodiments, a VGG16 algorithm can be pre-trained on ImageNet. In various embodiments, a pre-trained VGG16 algorithm can have its last three full-connected layers removed. In these embodiments, remaining layers of the pre-trained VGG16 algorithm can be treated as a fixed feature extractor for a training data set. In many embodiments, one or more additional layers (e.g., dense layers, batch normalization layers, dropout layers) can be added to a pre-trained VGG16 algorithm. These one or more additional layers can also be tuned and trained as described further herein. In this way, utilizing a pre-trained weights of VGG16 can not only reduce processing times for training a deep learning algorithm, but also reduce data needed to train the deep learning algorithm. 
     In many embodiments, method  400  can comprise an activity  403  of filtering a lifestyle image from data received from a repository of digital images. In some embodiments, data received from a repository of digital images can comprise a digital image as described above in activity  401  and/or a lifestyle image as described above in activity  402 . In the same or different embodiments, data received from a repository can be continually sent (e.g. streamed) using one or more of the communication protocols described above. 
     In many embodiments, method  400  can comprise an activity  404  of annotating one or more digital images. In some embodiments, annotating one or more digital images can comprise adding tags to the image, as described above. Activity  404  can occur after activity  401  and can include optional activities  405 ,  406 , and/or  407 . 
     In many embodiments, method  400  can optionally comprise an activity  405  of creating a digital embedding. In some embodiments, a digital embedding can be created for a digital image. In the same or different embodiments, annotating a digital image can comprise creating a digital embedding of a digital image. In some embodiments, annotating a digital image can comprise creating an embedding of an item displayed in the digital image. In many embodiments, an embedding can comprise a mapping of a discrete, categorical variable to a vector. In the same or different embodiments, an embedding of a digital image can comprise a vector and/or a set of vectors that describe the contents of the image. In various embodiments, an embedding can comprise a table containing numerical representations of vectors. In many embodiments, an embedding can be created using a machine vision algorithm. In some embodiments, a machine vision algorithm can comprise at least a portion of an image segmentation algorithm and/or an object detection algorithm. 
     In many embodiments, method  400  can comprise an activity  406  of combining a digital embedding with one or more tags of an item. In various embodiments, tags of an item can correspond to tags as described with reference to activities  401 , as described above. 
     In many embodiments, method  400  can comprise an activity  407  of storing a digital embedding. In some embodiments, a digital embedding can be stored in a data store configured to store high dimensional data. For example, high dimensional data can be stored in Facebook AI Similarity Search (AKA “Faiss”) and/or Elastic Search. In the same or different embodiments, high dimensional data can comprise data having a large number of features, thereby leading to “the curse of dimensionality.” In some embodiments, an embedding can be stored as a sparse representation in a data store configured to store high dimensional data. Storage efficiency can be improved by encapsulating embeddings into coarser, conceptual embeddings by storing them as a sparse representation. In some embodiments, a sparse representation of an embedding can store only non-zero counts for vectors in the embedding. This technique, then, can reduce required storage space, and can consequently make subsequent reading and/or processing of the sparse representation of an embedding faster than reading and/or processing of embeddings that are not stored as a sparse representation. In many embodiments, a sparse representation of an embedding can be stored in a datastore configured to store high dimensional data, as described above. In many embodiments, a tag of an item, as described above, can be stored in a data store configured to store high dimensional data. In the same or different embodiments, a tag of an item, as described above, can be combined with an embedding or a sparse representation of an embedding, and the combined data can then be stored in a data store configured to store high dimensional data. 
     In many embodiments, method  400  can comprise an activity  408  of digitally altering one or more digital images. Activity  408  can occur after activity  404 . In the same or different embodiments, a digital image can be annotated, as described above, before the digital image is digitally altered. In some embodiments, altering a digital image can comprise enlarging the digital image, shrinking the digital image, rotating the digital image, recoloring the digital image (e.g. converting to greyscale, converting to black and white, altering a color saturation of an image, etc.), flipping the image, mirroring the image, scaling the image, shearing the image and/or any other type of digital image manipulation known in the art of heretofore created. 
     In many embodiments, method  400  can comprise an activity  409  of digitally combining one or more digital images with at least one or more portions of one or more other digital images to create one or more combined digital images. Activity  409  can occur after activity  408  and can include optional activities  410  and/or  411 . In some embodiments, a portion of one or more digital images can comprise a piece of a digital image (e.g. one half, one third, three fourths, etc.). In the same or different embodiments, a portion of one or more digital images can comprise one or more images of a set of digital images. In the same or different embodiments, one or more combined digital images can comprise a combination of digitally altered images, as described above, and/or unaltered digital images. In various embodiments, a combined digital image can comprise multiple copies of the same image and/or copies of different images. In the same or different embodiments, a combined digital image can be a lifestyle image, as described above. In some embodiments, a combined digital image can be stored in a repository of digital images, as described in activity  401  above. 
     In many embodiments, method  400  can comprise activity  410  of initializing a digital canvas. In some embodiments, a digital canvas can comprise an image file configured to be editable by an image processing software. In various embodiments, an image file can comprise a joint photographic experts group (JPEG) image file, a JPEG file interchange format (JFIF) image, joint photographic experts group 2000 (JPEG 2000) image file, an exchange image file format (Exif) image file, a tagged image file format (TIFF) image file, a graphics interchange format (GIF) image file, a bitmap (BMP) image file, a portable network graphics (PNG) image file, a portable pixmap (PPM) image file, a portable graymap (PGM) image file, a portable bitmap (PBM) image file, a portable any map (PNM) image file, a WebP image file, a high-dynamic-range (HRD) image file (e.g., JPEG-HDR), a high efficiency image file format (HEIF) image file, a better portable graphics (BPG) image file, and/or any other image file types known in the art or heretofore created. In some embodiments, initializing a digital canvas can comprise opening one or more of the above referenced file formats with an image processing and/or image viewing software. In the same or different embodiments, initializing a digital canvas can comprise writing or creating one or more of the above referenced file formats de novo. In many embodiments, a digital canvas can be blank when initialized. In some embodiments, a digital canvas can have image data present when initialized. 
     In many embodiments, method  400  can comprise activity  411  of randomly distributing one or more digital images onto a digital canvas. In many embodiments, a random number can be assigned to each image of one or more digital images, and the random number can be used to distribute each image onto a digital canvas. In the same or different embodiments, the random number can be used in combination with a coordinate system overlaid onto the digital canvas to randomly distribute the one or more digital images onto the digital canvas. In various embodiments, a Poisson point process can be used to randomly distribute one or more digital images onto a digital canvas. In many embodiments, activity  411  can be modeled as a 2d packing problem (e.g., given a strip of finite width and infinite height, how can one pack items into the strip in ways that minimize the height used), and various heuristics used to solve the 2d packing problem can be used to randomly distribute one or more digital images onto a digital canvas. For example, a 2d packing problem can be solved using a strip packing algorithm, a bin packing algorithm, a hybrid first fit algorithm, a hybrid next fit algorithm, a floor-ceiling algorithms, a finite next-fit algorithm, a finite first fit algorithm, a finite bottom left algorithm, a next bottom-left algorithm, an alternate directions algorithm, etc. In the same or different embodiments, an X-Y coordinate system can be overlaid onto a digital canvas. In these embodiments, a random X-Y coordinate can be assigned to an image of one or more digital images, and the image can be placed on the digital canvas at the X-Y coordinate assigned to the image. In many embodiments, images distributed onto a digital canvas can be overlapped with at least a portion of one or more digital images. In the same or different embodiments, overlapping images on a digital canvas with at least a portion of one or more digital images can comprise placing one or more images on top of or below images on a digital canvas such that a portion of the images on the digital canvas and/or the one or more images are partially obscured. 
     In some embodiments, it can be desirable to create a more realistic and/or natural lifestyle image. In these embodiments, a set of lifestyle images can be analyzed to determine a rate of co-occurrence between two or more items (e.g., to determine a rate at which two or more items images occur together in the same lifestyle images). In the same or different embodiments, a set of lifestyle images can be analyzed to determine a positional bias of each item in the set of lifestyle images (e.g., which items displayed in a lifestyle image occur close to one another). In various embodiments, a rate of co-occurrence and a positional bias can be used to distribute images on a digital canvas in a semi-random fashion. In many embodiments, an image can be chosen for distribution on a digital canvas based upon a co-occurrence rate of the image with a previously chosen image. In the same or different embodiments, distributing stock images on a digital canvas in semi-random fashion can comprise biasing an assignment of an X-Y coordinate assigned to a stock image. In some embodiments, biasing an assignment of an X-Y coordinate can comprise restricting an X-Y coordinate assigned to an image such that a positional bias rate is satisfied and/or maintained. 
     In many embodiments, method  400  can comprise an activity  412  of training a machine learning algorithm on one or more combined digital images. In some embodiments, training a machine learning algorithm can comprise estimating internal parameters of a model configured to identify items shown in a digital image. Activity  412  can occur after activity  409 . In many embodiments, a digital image can comprise a stock image, a lifestyle image, and/or a combined digital image as described above. In embodiments where multiple items are present in a digital image, a machine learning algorithm can be trained to identify a portion of the multiple items and/or each item of the multiple items. In various embodiments, a machine learning algorithm can be trained using labeled training data, otherwise known as a training dataset. In many embodiments, a training dataset can comprise all or a part of digital images described, created, and/or annotated in activities  401 - 411 . In this way, a machine learning algorithm can be configured to identify an unknown item in a digital image. In the same or different embodiments, a machine learning algorithm can comprise a convolutional neural network (CNN). For example, a CNN can comprise an inception-V3 algorithm, a VGG16/19 algorithm, inception V1/V2/V3 algorithms, resnet (residual network) 50 algorithm, an inception-resnet algorithm, an AlexNet algorithm, a NASNet algorithm, etc. In the same or different embodiments, a pre-trained machine learning algorithm can be used, and the pre-trained algorithm can be re-trained on the labeled training data. 
     In many embodiments, method  400  can comprise an activity  413  of storing a machine learning algorithm in one or more non-transitory storage devices. In some embodiments, a machine learning algorithm can be stored in a data store configured to store high dimensional data and/or as a sparse representation, as described above with reference to activity  407 . In the same or different embodiments, a stored machine learning algorithm can be accessed by a computer system (e.g., computer system  100  ( FIG. 1 )), and then used to identify unknown items in digital images. In various embodiments, using a stored machine learning algorithm can comprise a method similar and/or identical to method  600  ( FIG. 6 ). 
     Turning ahead in the drawings,  FIG. 5  illustrates a flow chart for a method  500 , according to an embodiment. Method  500  is merely exemplary and is not limited to the embodiments presented herein. Method  500  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the activities of method  500  can be performed in the order presented. In other embodiments, the activities of method  500  can be performed in any suitable order. In still other embodiments, one or more of the activities of method  500  can be combined or skipped. In various embodiments, method  500  can be performed after and/or simultaneously with method  400  ( FIG. 4 ). In many embodiments, system  300  ( FIG. 3 ) can be suitable to perform method  500  and/or one or more of the activities of method  500 . In these or other embodiments, one or more of the activities of method  500  can be implemented as one or more computer instructions configured to run at one or more processing modules and configured to be stored at one or more non-transitory memory storage modules. Such non-transitory memory storage modules can be part of a computer system such as web server  310  and/or user computers  330 ,  331 . The processing module(s) can be similar or identical to the processing module(s) described above with respect to computer system  100  ( FIG. 1 ). 
     In many embodiments, method  500  can comprise activity  501  of receiving one or more lifestyle images from a repository of digital images. As stated above with reference to method  500 , in some embodiments, activity  501  can be performed after and/or simultaneously with method  400  ( FIG. 4 ). In many embodiments, one or more lifestyle images can comprise a lifestyle image as described above with regards to activities  401 - 404  ( FIG. 4 ) and/or a combined digital image as described in activities  409 - 411  ( FIG. 4 ). In this way, a machine learning algorithm can be tested on an image comprising known items with known embeddings. 
     In many embodiments, method  500  can comprise activity  502  of analyzing one or more lifestyle images using a machine learning algorithm to identify at least a portion of more than one item. In some embodiments, a machine learning algorithm can comprise a machine learning algorithm as stored in activity  413  ( FIG. 4 ). In the same or different embodiments, analyzing one or more lifestyle images to identify items in the one or more lifestyle images can comprise determining a digital embedding for the one or more lifestyle images. In various embodiments, a digital embedding can be determined for an image as a whole or for portions of an image (e.g. portions of the image containing one or more items of interest). In some embodiments, a digital embedding can be determined as descried in activity  405  ( FIG. 4 ). 
     In many embodiments, method  500  can comprise an activity  503  of comparing an identification of at least a portion of more than one item with an identification of each item of the more than one item. In some embodiments, an identification of an item can comprise an embedding, as described above. In the same or different embodiments, comparing an identification can comprise comparing an embedding, as determined in activity  503 , with an embedding as determined in activities  405 - 407  ( FIG. 4 ). In many embodiments, a similarity between two embeddings can be determined. In the same or different embodiments, when a similarity between two embeddings is above a predetermined threshold, it can be determined that a machine learning algorithm correctly identified an item present in a lifestyle image. 
     In many embodiments, method  500  can comprise an activity  504  of determining an accuracy of a machine learning algorithm. In some embodiments, an accuracy of a machine learning algorithm can be determined by summing a number of correct identifications of items present in a lifestyle image, and then by dividing the number of correct identifications by a total number of identification attempts. In the same or different embodiments, an accuracy of a machine learning algorithm can be determined by summing a number of incorrect identifications of items present in a lifestyle image, and then by dividing the number of correct identifications by a total number of identification attempts. 
     Turning ahead in the drawings,  FIG. 6  illustrates a flow chart for a method  600 , according to an embodiment. Method  600  is merely exemplary and is not limited to the embodiments presented herein. Method  600  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, the activities of method  600  can be performed in the order presented. In other embodiments, the activities of method  600  can be performed in any suitable order. In still other embodiments, one or more of the activities of method  600  can be combined or skipped. In various embodiments, method  500  can be performed after and/or simultaneously with method  400  ( FIG. 4 ) and/or method  500  ( FIG. 5 ). In many embodiments, system  300  ( FIG. 3 ) can be suitable to perform method  600  and/or one or more of the activities of method  600 . In these or other embodiments, one or more of the activities of method  600  can be implemented as one or more computer instructions configured to run at one or more processing modules and configured to be stored at one or more non-transitory memory storage modules. Such non-transitory memory storage modules can be part of a computer system such as web server  310  and/or user computers  330 ,  331 . The processing module(s) can be similar or identical to the processing module(s) described above with respect to computer system  100  ( FIG. 1 ). 
     In many embodiments, method  600  can comprise an activity  601  of receiving a digital image. In some embodiments, a digital image can comprise multiple items. In various embodiments, a digital image can comprise a stock image, a combined digital image, and/or a lifestyle image, as described above with reference to activities  401 - 411  ( FIG. 4 ). In various embodiments, a digital image can be received from an electronic device of a user, such as user computer  330 ,  331 . In the same or different embodiments, a digital image can be taken using a camera coupled to an electronic device of a user. 
     In many embodiments, method  600  can comprise an activity  602  of determining an embedding for a digital image. In some embodiments, an embedding can be determined using a machine learning algorithm. In the same or different embodiments, the machine learning algorithm can be the same as a machine learning algorithm described in activity  412  ( FIG. 4 ). 
     In many embodiments, method  600  can comprise an activity  603  of identifying an item in a digital image. In some embodiments, a digital image can be a digital image as described in activities  401 - 411  ( FIG. 4 ) and/or activity  601 . In the same or different embodiments, an item in a digital image can be identified as described in activity  502  ( FIG. 5 ). In many embodiments, activity  603  can optionally comprise an activity  604  of comparing at least a portion of an embedding for a digital image with at least one embedding stored in a data store. In the same or different embodiments, a data store can comprise a data store configured to store high dimensional data, as described in activities  407  and/or  413 . 
     In many embodiments, method  600  can comprise an activity  605  of facilitating an alteration of a GUI. Activity  605  can occur after activity  603 . In some embodiments, a GUI can comprise GUI  340 ,  341 . In the same or different embodiments, activity  605  can be performed in response to identifying the item in the digital image, as described in activities  502  ( FIG. 5 ) and/or  603 . In many embodiments, altering a GUI can comprise customizing a content of a GUI. In the same or different embodiments, customizing a content on a GUI can comprise altering an image displayed on the GUI, altering text on the GUI, altering a layout of the GUI, changing a type of the GUI, displaying an advertisement on the GUI, displaying no advertisement on the GUI, altering a color displayed on the GUI, etc. In many embodiments, customizing a content on a GUI can comprise displaying certain content at specific times. In further embodiments, a content on a GUI can comprise advertisements for products, services, and/or events. In some embodiments, facilitating an alteration of a GUI can comprise embedding a selectable element in a digital image. In various embodiments, facilitating an alteration of a GUI can comprise overlaying, over a digital image, a selectable element. In many embodiments, an area of a selectable element can be determined using the CNN, as described above. In the same or different embodiments, a selectable element can comprise a hyperlink that, when selected, navigates an electronic device to a webpage displaying an item displayed in the digital image. In many embodiments, the webpage can be configured to offer the item for sale to the user. In the same or different embodiments, altering a GUI can comprise facilitating a display of an item similar to an item in a digital image (e.g., an item of a different size, color, style, etc.). In many embodiments, altering a GUI can comprise facilitating a display of an item complimentary to an item in a digital image (e.g., an item of a complementary size, color, style, etc.). 
     Turning ahead in the drawings,  FIG. 7  illustrates a block diagram of a system  700  that can be employed for behavior based messaging. System  700  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  700  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  700  can perform various procedures, processes, and/or activities. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  700 . 
     Generally, therefore, system  700  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  700  described herein. 
     In many embodiments, system  700  can comprise non-transitory memory storage module  701 . Memory storage module  701  can be referred to as image receiving module  701 . In many embodiments, image receiving module  701  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  401  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  702 . Memory storage module  702  can be referred to as image identifying module  702 . In many embodiments, image identifying module  702  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  402  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  703 . Memory storage module  703  can be referred to as image filtering module  703 . In many embodiments, image filtering module  703  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  403  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  704 . Memory storage module  704  can be referred to as image annotating module  704 . In many embodiments, image annotating module  704  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  404  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  705 . Memory storage module  705  can be referred to as embedding creating module  705 . In many embodiments, embedding creating module  705  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  405  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  706 . Memory storage module  706  can be referred to as embedding combining module  706 . In many embodiments, embedding combining module  706  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  406  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  707 . Memory storage module  707  can be referred to as embedding storing module  707 . In many embodiments, embedding storing module  707  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  407  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  708 . Memory storage module  708  can be referred to as image altering module  708 . In many embodiments, image altering module  708  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  408  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  709 . Memory storage module  709  can be referred to as image combining module  709 . In many embodiments, image combining module  709  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  409  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  710 . Memory storage module  710  can be referred to as canvas initializing module  710 . In many embodiments, canvas initializing module  710  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  410  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  711 . Memory storage module  711  can be referred to as image distributing module  711 . In many embodiments, image distributing module  711  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  411  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  712 . Memory storage module  712  can be referred to as machine learning algorithm training module  712 . In many embodiments, machine learning algorithm training module  712  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  412  ( FIG. 4 )). 
     In many embodiments, system  700  can comprise non-transitory memory storage module  713 . Memory storage module  713  can be referred to as machine learning algorithm storing module  713 . In many embodiments, strict model display module  713  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  400  ( FIG. 4 ) (e.g., activity  413  ( FIG. 4 )). 
     Turning ahead in the drawings,  FIG. 8  illustrates a block diagram of a system  800  that can be employed for behavior based messaging. System  800  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  800  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  800  can perform various procedures, processes, and/or activities. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  800 . 
     Generally, therefore, system  800  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  800  described herein. 
     In many embodiments, system  800  can comprise non-transitory memory storage module  801 . Memory storage module  801  can be referred to as lifestyle image receiving module  801 . In many embodiments, lifestyle image receiving module  801  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  500  ( FIG. 5 ) (e.g., activity  501  ( FIG. 5 )). 
     In many embodiments, system  800  can comprise non-transitory memory storage module  802 . Memory storage module  802  can be referred to as lifestyle image analyzing module  802 . In many embodiments, lifestyle image analyzing module  802  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  500  ( FIG. 5 ) (e.g., activity  502  ( FIG. 5 )). 
     In many embodiments, system  800  can comprise non-transitory memory storage module  803 . Memory storage module  803  can be referred to as identification comparing module  803 . In many embodiments, identification comparing module  803  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  500  ( FIG. 5 ) (e.g., activity  503  ( FIG. 5 )). 
     In many embodiments, system  800  can comprise non-transitory memory storage module  804 . Memory storage module  804  can be referred to as accuracy determining module  804 . In many embodiments, accuracy determining module  804  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  500  ( FIG. 5 ) (e.g., activity  504  ( FIG. 5 )). 
     Turning ahead in the drawings,  FIG. 9  illustrates a block diagram of a system  900  that can be employed for behavior based messaging. System  900  is merely exemplary and embodiments of the system are not limited to the embodiments presented herein. System  900  can be employed in many different embodiments or examples not specifically depicted or described herein. In some embodiments, certain elements or modules of system  900  can perform various procedures, processes, and/or activities. In these or other embodiments, the procedures, processes, and/or activities can be performed by other suitable elements or modules of system  900 . 
     Generally, therefore, system  900  can be implemented with hardware and/or software, as described herein. In some embodiments, part or all of the hardware and/or software can be conventional, while in these or other embodiments, part or all of the hardware and/or software can be customized (e.g., optimized) for implementing part or all of the functionality of system  900  described herein. 
     In many embodiments, system  900  can comprise non-transitory memory storage module  901 . Memory storage module  901  can be referred to as digital image receiving module  901 . In many embodiments, digital image receiving module  901  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  600  ( FIG. 6 ) (e.g., activity  601  ( FIG. 6 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  902 . Memory storage module  902  can be referred to as embedding determining module  902 . In many embodiments, embedding determining module  902  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  600  ( FIG. 6 ) (e.g., activity  602  ( FIG. 6 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  903 . Memory storage module  903  can be referred to as item identifying module  903 . In many embodiments, item identifying module  903  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  600  ( FIG. 6 ) (e.g., activity  603  ( FIG. 6 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  904 . Memory storage module  904  can be referred to as embedding comparing module  904 . In many embodiments, embedding comparing module  904  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  600  ( FIG. 6 ) (e.g., activity  604  ( FIG. 6 )). 
     In many embodiments, system  900  can comprise non-transitory memory storage module  905 . Memory storage module  905  can be referred to as alteration facilitating module  905 . In many embodiments, alteration facilitating module  905  can store computing instructions configured to run on one or more processing modules and perform one or more acts of method  600  ( FIG. 6 ) (e.g., activity  605  ( FIG. 6 )). 
     Although systems and methods for identifying items in a digital image have been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes may be made without departing from the spirit or scope of the disclosure. Accordingly, the disclosure of embodiments is intended to be illustrative of the scope of the disclosure and is not intended to be limiting. It is intended that the scope of the disclosure shall be limited only to the extent required by the appended claims. For example, to one of ordinary skill in the art, it will be readily apparent that any element of  FIGS. 1-9  may be modified, and that the foregoing discussion of certain of these embodiments does not necessarily represent a complete description of all possible embodiments. For example, one or more of the procedures, processes, or activities of  FIG. 4  may include different procedures, processes, and/or activities and be performed by many different modules, in many different orders. 
     All elements claimed in any particular claim are essential to the embodiment claimed in that particular claim. Consequently, replacement of one or more claimed elements constitutes reconstruction and not repair. Additionally, benefits, other advantages, and solutions to problems have been described with regard to specific embodiments. The benefits, advantages, solutions to problems, and any element or elements that may cause any benefit, advantage, or solution to occur or become more pronounced, however, are not to be construed as critical, required, or essential features or elements of any or all of the claims, unless such benefits, advantages, solutions, or elements are stated in such claim. 
     Moreover, embodiments and limitations disclosed herein are not dedicated to the public under the doctrine of dedication if the embodiments and/or limitations: (1) are not expressly claimed in the claims; and (2) are or are potentially equivalents of express elements and/or limitations in the claims under the doctrine of equivalents.