Method, medium, and system for creating a filtered image set of a product

Systems and methods for collecting, selecting, and displaying an image or image set in a network based environment are described. The systems and methods can collect multiple images for any given item from multiple sources, select a desired image (or set of images) that best depicts that item, and then display that selected image (or image set) in the network based environment. The desired image (or image set) that best depicts the item can be selected using any number or combination of pre-selected criteria. By using the pre-selected criteria, the process needs no manual intervention, and can therefore be automated or semi-automated to save both time and cost.

FIELD

This application relates generally to systems and methods for collecting, selecting, and displaying images in a network based environment. In particular, this application relates to systems and methods for selecting a specific image(s) among a collection of images using pre-selected criteria and then displaying the selected image in a network based environment.

BACKGROUND

Digital imaging has become increasingly popular, especially in electronic commerce which is increasingly being used by sellers to conduct business and sell items to customers. Customers are able to efficiently view and purchase a wide variety of items, including both goods and services, over computer networks, including the Internet. The same goods and services can be offered by multiple sellers, each with its own description of the item for sale (i.e., including an image), allowing a customer to quickly and easily select any desired item from any given seller by using each seller's image.

The amount of image files being used for such purposes is increasing dramatically. But with the increasing number of image files comes an increasing number of duplicate images, as well as minor variations between different image files meant to identify the same item. Indeed, the minor variations between image files can create apparent duplication of subject matter that is difficult to distinguish when a customer is looking for something specific. In certain instances, the different images (or image sets) for the same item can even be provided by the same source, increasing confusion by the consumer. To avoid that confusion, one option has been to present all images meeting the criteria used to identify the item. This option is distracting and time consuming because the customer must look at every single image in the purchasing process.

Thus, from a customer's perspective, it would be helpful if only a single image (or image set) is displayed since this would simplify the viewing and purchasing process for the customer. In most instances, that single image (or image set) that is displayed to the customer should accurately depict the item. But often the different images (or image sets) are provided by different sellers, and processed by multiple sellers each with their own conditions and criteria, before being displayed to the customer. The different images, not to mention the different conditions and criteria, can further confuse the customer and complicate the purchasing process.

So there exists a problem of how to select a single image that best depicts the item for sale. Most methods address this problem by manually selecting the best image. But it is not unusual in these methods to be confronted with thousands of images. And requiring someone to manually filter through thousands of images is onerous, not to mention time consuming and expensive.

Some methods address this problem by selecting the images based on non-image based characteristics, such as the date or title accompanying the images. But selecting the images to display to the customer using such methods often does not result in selecting the single image (or image set) that best depicts the item.

SUMMARY

Systems and methods for collecting, selecting, and displaying an image or image set in a network based environment are disclosed. The systems and methods can collect multiple images for any given item from multiple sources, select a desired image (or set of images) that best depicts that item, and then cause the selected image (or image set) to be displayed in the network based environment. The desired image (or image set) that best depicts the item can be selected using any number or combination of pre-selected criteria. By using the pre-selected criteria, the process needs no manual intervention, and can therefore be automated or semi-automated to save both time and cost.

Together with the following description, the Figures demonstrate and explain the principles of the systems and methods for collecting, selecting, and displaying an image or image set in a network based environment. In the Figures, the size and configuration of components may be exaggerated for clarity. The same reference numerals in different Figures represent the same component.

DETAILED DESCRIPTION

The following description supplies specific details in order to provide a thorough understanding. Nevertheless, the skilled artisan would understand that the systems and associated methods of using the systems can be implemented and used without employing these specific details. Indeed, the systems and associated methods can be placed into practice by modifying the illustrated systems and methods and can be used in conjunction with any apparatus and techniques conventionally used in the industry. For example, while the description below focuses on systems and methods for selecting an image to display in a network-based environment for electronic commerce, it can be implemented in many other end uses, such as brick and mortar retailers that have a computerized means for shopping, whether via a network or within the stores themselves, as well as in non-electronic commerce applications.

FIG. 1contains a pictorial diagram of a sample system100for collecting, selecting, and displaying an image or image set in a network environment. InFIG. 1, the system100contains an imaging server116that facilitates a customer viewing images on any number or combination of customer devices102(a) . . .102(n). The images are collected from any number or combination of sources114(a) . . .114(n), stored in a data store124, and analyzed in an image information server112. The system100also contains a back-end interface118for an operator or administrator to administer the system as known in the art.

Prior to discussing the details of system100, it should be understood that the following description is presented largely in terms of steps and operations that may be performed by conventional computer components. These computer components, which may be grouped in a single location or distributed over a wide area, generally include computer processors, memory storage devices, display devices, input devices, etc. In circumstances where the computer components are distributed, the computer components are accessible to each other via any known communication links, such as those illustrated inFIG. 1. The system100could equally operate within a computer system having a fewer or greater number of components than those illustrated inFIG. 1. Thus, the depiction of system100should be taken as illustrative and not limiting. For example, the system100could implement various services components and peer-to-peer network configurations to implement at least a portion of the processes.

The system100contains a computer network110for connecting all the components. Computer networks are well known in the field of communications. Computer networks may include communication links that extend over a local area or a wide area, or even be global, as in the case of computer networks forming the Internet. In some embodiments, computer network110comprises the Internet. Protocols and components for communicating via such networks and the Internet are well known to those skilled in the art of computer communications and, thus, need not be described in more detail herein. Those skilled in the art will recognize that other interactive environments that include local or wide area networks that connect users and data stores can be used in the system100.

The customer devices that can be used in the system100can be any computing device that are capable of communicating over computer network110. Examples of such computing devices include set-top boxes, personal digital assistants, wireless telephones, media players, web pads, electronic book readers, tablets, laptop computers, desktop computers, or combinations thereof. InFIG. 1, the illustrated customer devices are depicted as a personal computer102(a), a personal digital assistant (PDA)102(b), and a wireless telephone102(n).

The imaging server116is generally responsible for providing front-end communication with the customer devices. This communication may include generating text and/or graphics, possibly organized as an interface using hypertext transfer or other protocols in response to information inquiries received from the various customer devices. The imaging server116may also obtain information about any image(s) from the imaging information server112and display that information to the customer devices. For example, the imaging server116may obtain information about an image which best depicts an item that a customer is looking to purchase and then display that image on the PDA102(b).

The image information server112that contains data about the various images available. The data contained in server112includes all of these images, as well as the various sources (collectively,114(a) . . .114(n)) that have provided those images. The data contained in server112can also store any desired information that is related to the images themselves, such as any metadata that is associated with an image. The image information server112can be updated in real time and may report a real time inventory of the various images, image sources, and related information.

As shown inFIG. 1, the image information server112can obtain images from any number of sources114. Those sources can include any entity that provides any image about any item. Typically, those sources include sellers that are offering various items for sale that can be purchased by the customer. These sources can also include sellers of the images themselves, rather than an item depicted by the images. One example of such sellers would include photographers.

The image information server112in system100is generally responsible for maintaining any number or combination of rules (or criteria) for comparing images and for selecting a single image or image set to display. These rules (or criteria) may be maintained in any conventional data store in one or more memory storage devices within the image information server112. These rules (or criteria) are used to select a best image (or images) for display, with a best image (or images) being that image(s) which is optimized for its intended use. The image information server112can be updated in real time and may report real time information about the number or combination of such rules.

The data store124stores the images and any desired data about the images. The images would include any multimedia file or digital file in any format, including two dimensional standard images, three dimensional images, sounds, and videos (as well along with the accompanying codecs). The images may be stored in compressed and/or uncompressed states. Video images may be clipped or divided into smaller components as needed or desired in consideration of network resources.

The systems described above can be used to carry out many methods for collecting, selecting, and displaying an image or image set in a network based environment. For example, the systems described above can collect multiple images for any given item from multiple sources, select a single image (or set of images) that best depicts that item, and then display that best image (or image set). An image set may comprise only a single image or a plurality of images. The image can be displayed in any type of user interface (UI), including a UI presented to an operator of the system or to a customer looking to purchase an item.

FIG. 2illustrates one example of a UI that may be presented by the system to an operator. The UI inFIG. 2depicts images of a fashionable woman's boot, but any item could be displayed by the UI. The UI130comprises a primary view area132for viewing the image or images returned by the imaging server. The UI130also contains a text portion134, which is an area for a textual description or textual input. InFIG. 2, the subject of the image in the primary viewing area132is textually described in the text portion134. In some instances, the UI130may contain alternate viewing areas136, which are areas containing images different from the image in the primary view area. The skilled artisan would understand that the shapes and sizes of the various components in UI130could be changed as needed.

To be displayed in a UI (such as that depicted inFIG. 2), the image(s) must be scored and then the desired image(s) for display must be selected. One example of a method for scoring the image(s) includes the exemplary method150illustrated inFIG. 3. Although the method150inFIG. 3will be discussed in successive steps, it may be done in alternate orders and should not be understood as having to be performed in the described order below. A method of fewer or more steps than those depicted may also be used.

As depicted inFIG. 3, the method150begins in block155when the image scoring is initiated by the image information server112. The method150continues in block160when an image request for an item is received. The request can be received by either the operator who is analyzing a particular item or a customer who is looking to purchase a particular item. After the request is received, the image metrics for each item are collected in block165. The items can be collected from any source that can provide any type of image for the item. In most instances, those sources are sellers of the item that is under consideration. But they can include any source that can provide the item in any form, including sellers of the images themselves rather than sellers of items represented by the images. The images that are collected can be any view of the item under consideration. In some instances, the image might be a single view of an item or multiple views of the same item. In other instances, the image can be a series (or set) of views of an item. In yet other instances, the image can be a view of multiples items, such as several components that can be sold separately or that can be packaged together and sold as a single item. In still other instances, the images can be views of specific parts or aspects of the item.

The images that are collected can be isolated or can have other information associated with them. When isolated, only the image itself is collected. In most instances, though, the images will have other information associated with it. For example, an image might have text (such as a title) associated with it. As another example, the image may have other non-image information (other than text) associated with it. As yet another example, the image may have metadata associated with it. Any known metadata can be associated with the image, including merchant information (i.e., a merchant rating), digital rights management restrictions, and/or customer information (i.e., a customer rating).

The images that are collected can depict just the item itself. As well, the image can depict the item in some type of context. For example, the image could display the item (clothing) and how it is typically used (being worn on a person or on a mannequin). As another example, the image can depict the item (a piece of art) as it would normally be displayed (in an art gallery). As well, the item could be displayed in any type of desired background or environment.

The collection of images need not be a complete collection of every available image from every available source. In some embodiments, the collection could be merely a category or subset of images. The subset of images could be, for example, from a selected seller or a favorite image source.

After the image metrics have been collected in block165, each image is scored in block170using the metrics. The scoring of the metrics is the process, either automatic or manual, of selecting and ranking the images. In some instances, the scoring process determines which image is best given the available metrics. In the some embodiments, a predetermined metric (such as a score for the image assigned by the source from which the image is obtained) may be selected and then matched to the image. In other embodiments, though, algorithms can be used to provide the metrics for scoring the images. The images can be judged against any number or combination of metrics that are designed to find the best image or image set for a particular item.

In some instances, the metrics themselves are scored before they are used to score the images. The scoring of the metrics is the process, either automatic or manual, of selecting and ranking the metrics using any number or combination of criteria. In the most embodiments, a predetermined criterion may be selected and then matched to a metric. In other embodiments, algorithms provide the basis for selecting the metrics. In some instances, the criteria are pre-selected so that little to no manual intervention is needed in the selection process. In other instances, though, the criteria are selected manually or on a semi-automated basis. For example, the metrics can be evaluated for any reason, including determining whether they are working and selecting the desired image or best image.

The number and types of metrics used to select the desired image will depend on many parameters, including the type of item and the specific image desired. The number and types of metrics used, while theoretically unlimited, are most often constrained by the time and effort—whether manual or automatically—needed to perform the selection process.

The scoring block170could include a procedure for classifying or categorizing the images. In some embodiments, this categorization procedure would simplify the scoring performed in block170process by requiring that only a certain category of images needed to be analyzed for selection. For example, where the desired image is actually a set of images showing all possible views of an item, categorizing all images as an image set, or not an image set, would allow the selection process to only be performed on those images that are part of an image sets.

The scoring performed in block170can be based on any desired number or combination of metrics. The actual metrics can be either objective or subjective, whether they are subjective to the customer or subjective to the operator. One example of the metrics that can be used include those related to the quality of the image, such as the number of pixels, the size of the image file, the size of the actual image (small, medium, large, etc. . . . ), the granularity, and the like. Other examples of the metrics include the characteristics of the actual image, such as the shape of the image (square, circular, oval, polygonal, etc. . . . ), the hue, the color or colors of the image, and the like. Yet other examples of the metrics include the information associated with the image, such as the metadata, merchant scoring, digital rights management (DRM) restrictions, the originating source, and the like.

These metrics can be combined and considered in any desired manner in block170. As well, the metrics can be ranked in any desired manner so that some metrics are given more importance than others. As well, there can be any trade-off (or scaling system) of the metrics that can be used. For example, the scoring can have a trade-off between the coherence and the completeness in selecting the desired image (as described in detail below).

In some embodiments, the metrics are pre-selected so that no manual intervention is needed. In these embodiments, the metrics can also be automatically ranked so that the scoring is automated and the ranking system automatically ranks the images based on the ranked metrics. This automatic ranking can be very advantageous because the scoring in block170can be automated or semi-automated, thereby allowing it to be carried out on a real time (or substantial real time) basis.

Once the scoring in block170has been performed, an image set is assembled in block175. The image set is assembled once the desired image has been determined to qualify under the scoring that has been performed in block170using the desired metric(s).

The assembled image sets can then optionally be checked for coherency as shown in block180. The coherence measures the ability of the image or image set to accurately portray the item. The coherence can be measured as a range or a grade of accuracy. The coherency may take any form depending only on the criteria used to determine whether the portrayal is accurate. For example, if the item represented by the image is a black shirt, the criteria selected may be the color of the image. In this example, coherence of the image set can be measured by determining whether the color of the shirt is depicted as grey or black. If the color is displayed as grey (when the item is black), the image set has failed the coherence check.

The assembled image set can also be optionally checked for completeness in block185. A complete image set may be dependent on the subject matter of the images or the images themselves. In some instances, the completeness may be judged as whether a set of images includes all pertinent views of the item for sale. In other instances, the completeness of the image can be judged on the degree to which the image shows the desired part(s) or attribute(s) of item, or even the degree to which the entirety of the item is depicted by the image.

Once the qualifying image sets are determined by the image information server, those sets are then stored in a data store, such as data store124of system100, in block190. The image information server112can then retrieve the qualifying image sets from the data store124whenever requested, i.e., when requested by the image server116.

After the image(s) have been scored using the above methods, they can then be selected for display. One exemplary method for image selection (including optimization for the best image that depicts an item) is depicted as method300inFIG. 4. Although the exemplary method300inFIG. 4will be discussed in successive steps, it may be done in alternate orders and should not be understood as having to be performed in the described order. A method of fewer or more steps than those depicted may also be used.

The image selection method300begins in block302when the image selection is initiated by the image server116. The image server116can be triggered automatically when a customer requests a desired item. The imaging server116can be also triggered manually by the operator.

The image selection method300continues in block305when a request for an image set is received by the imaging server116. That request can originate from the customer when the customer selects a desired item for purchase. As well, that request can originate from the operator when the operator triggers the imaging server116manually.

The method300continues in block306where the available image sets are retrieved. The image sets can be retrieved from a data store that is internal to the system100(i.e., data store124) or from an external data store. As an example of an external data store, individual sources (114(a) . . .114(n)) can submit electronic catalogs of images. The catalog may contain information about both the quality and non-quality aspects of images for the items available from that source, as well as any other information about the source. When the image set is retrieved from data store124, it is performed by the image information server112.

Next, the image sets that have been retrieved are then filtered, as shown in block310. In block310, the filter(s) is selected and then used to screen for the desired image, such as an optimal image (or image set) that best depicts the item. The filter(s) used in block310can be any one or combination of metrics or image selection criteria described above, including completeness, ranking, image type, etc.

In some instances, block310can be performed by parsing the metadata headers. This parsing of the headers can be accomplished by stepping through the metadata, discarding the data not desired, and saving the desired data. Alternatively, each source may present the metadata in a form that is immediately accessible and useable in this step. Under either method, the result is a comparable data set that can directly be run against the filter(s).

In other instances, block310can be performed by applying the filter(s) using any form of automatic or manual comparison process. In these instances, the image data (whether metadata or subject matter data) is compared to the filter on an image by image (or image set by image set) basis. This comparison can either be done manually using a person to perform the comparison, or automatically where the image server116performs the comparison, e.g., using an algorithm. In light of these options, block310may contain a control setting that allows the automatic or manual method, or some hybrid of the two, to be selected and performed.

As an example of the automatic comparison, the operator may want images of red flowers. Images of flower image data sets would then be retrieved from the data store124by the image information server112and presented to image server116. The image server116would then parse through the image set looking for images that are predominantly red. The image server116would then access any given image and analyze all the red pixels in the image, or portion of the picture, and store the pixel count in a data set. If desired, a second (or third or fourth) criterion could be used for further refinement by, for example, limiting the hue of red in each image or by requiring that over 50% percent of the pixels in the image must be red. For the latter criteria, the image server116could divide the red pixels by total pixels present in the images and then list the ratio in the data set.

The manual comparison can be especially useful because some image characteristics lend themselves to manual review because they are less quantifiable, or are even subjective characteristics. Examples of these characteristics include whether the image is in focus, whether the image contains the desired view(s), whether the image is accurate, and the like. By manually choosing to compare the images, the control of aspects that are subjective (or less quantifiable) can be maintained. Indeed, it would take considerable programming effort and potential use of artificial intelligence (AI) to perform some tasks that are very simple for a person to perform. The manual input needed for the manual selection can be performed by the operator of the system, a customer, or even personnel external to the system (i.e., on a contractual basis). The manual input in the selection process can then be combined with other automatic input and used to perform the manual comparison in block310.

Once the image set is assembled and filtered, the method300may return the best image for display to a user (whether an operator or a customer), as shown in block315. That best image is then stored in the data store124where it can be recalled at any time by the image server116to be displayed.

The purpose of the image selection method300is to provide a best image to display. The best image can then optionally be displayed in any type of user interface (UI), including a UI presented to an operator, or to a customer looking to purchase an item using the image. An example for displaying the best image in an operator UI is depicted inFIG. 2. The UI for a user could also contain any other known components, such as allowing the operator to manually swap the displayed image for any other image and an option for the operator to change the metrics used to select the desired image.

An example of a UI for a customer is depicted inFIG. 5. InFIG. 5, the customer UI230contains similar components as the operator UI depicted inFIG. 2. But the customer UI could also contain a price ($99.99) in the text portion134, as well as fields161,164,162, and166for the customer. These fields may be in selectable thumbnail161that, when selected, exchanges that image in the alternate viewing area with the image in the primary viewing area132. The customer UI can provide fields for purchasing162an item for which a desired image is displayed, or could provide options for selecting a different item164. Additionally, alternate or refining search criteria can be provided by search field166. The UI for a customer may contain any other known components, such as an option for the customer to select preferred display criteria.

The customer UI and/or the operator UI can contain options about the image being displayed and what other processes can be performed on—or with—the image. In some embodiments, the options can allow the image to be manipulated, for example, to rotate views of the item provided by an image set. In other embodiments, however, the UI merely allows the displayed image to be viewed and no additional options can be displayed. As well, the number of images to be displayed in the UI can be changed or otherwise modified.

In some embodiments, the customer UI and/or the operator UI can contain a number of different images that can be displayed in the UI, along with the underlying criteria that lead to the selection of any given image of those displayed. If too many images are selected in these embodiments, an additional response query may be made for further criteria in order to return a more manageable result of images. This step may be repeated until a desired image or image set is found.

Both the image scoring method150(illustrated inFIG. 3) and the image selection method300(illustrated inFIG. 4) can contain an optional feedback loop(s). The feedback loop(s) allows the methods, including any individual part, to be improved on using any feedback. One example of such feedback can be the manual feedback about the desired image provided by manual input. Another example of such feedback could be the feedback provided by a customer through, for example, a comment about the selected image or the image selection process. Another example of the feedback could include data about how often (or how little) a selected image for an item is viewed and/or used, for example, in a purchase by a customer. The feedback can be incorporated into the UI by inserting an appropriate field into the UI, such as field168that has been added to the operator UI130, as depicted inFIG. 6.

FIG. 7illustrates an example of how the above systems and methods can be used by an operator (or administrator) to select a specific image for a given item before that item is offered for sale to a customer.FIG. 7contains a flow chart depicting a method400for finding an image for a black Polo brand T-shirt that will be displayed to a customer. The method400begins at block402when the image server116starts by being triggered manually by the operator.

The method400continues in block403when all colors of Polo brand T-shirts can be scored since the operator knows that the item of interest is a black Polo brand T-shirt. The method continues in block404when the item of interest (a black Polo brand T-shirt) is selected by the operator from among all of the colors of the Polo brand T-shirts that have been scored in block403. Next, a data set of image sources (including sellers or suppliers) that have at least one image of a black Polo brand T-shirt can be compiled in block406. In this method400, those image sources will most likely comprise any seller that is offering a black Polo brand T-shirt for sale to a customer.

The method400continues when the operator enters the desired metrics or criteria for the images of a black Polo brand T-shirt in block408. The operator will, of course, seek an image that best appeals to customers and so will select those metrics or criteria that, when used, will select the most appealing black Polo brand T-shirt. For example, the operator may select one of the criteria to be that the image has a white background since that will best show the black color of the Polo brand T-shirt.

A data set of all the images for black Polo brand T-shirts by all the sources is then collected in block410. This data set can be collected automatically from any of the available image sources compiled in block406. The data set can then be organized in any manner known in the art, including organizing by the file size or resolution of the image files.

A best or optimal image (or set of images) for the black Polo T-shirt is then selected for display in block412. The optimal image can be selected by comparing the criteria from block408that have been collected. An example of an optimal image (or image set) could include only an image set where all of the images have a white background (since that criteria was selected as an example for block408). Another example could be a black Polo T-shirt that is shown as being worn on a model, and a complete set of images showing all views of that specific black Polo T-shirt.

Optionally, a preference can be given to a given image that is provided from a specific source, as shown at block414. For example, source C could be manually chosen over source A and B because of a contractual arrangement or because of the previous quality of images provided by source C. As well, source C could be automatically given preference because source C could have more images of black Polo T-shirts in the data set(s) than sources A or B. Any other reason known in the art for preferring one source over another could also be used.

In block418, the best image set is displayed in a UI. The UI may include an area for the selected image of the black Polo T-shirt and secondary areas for related or alternative images, as described above. The operator's selection of different options in block420determines whether the method400proceeds or stops. In one option, the operator can further refine the image process by adding more criteria, subtracting criteria, replacing criteria, or selecting different criteria. When this option is selected by the operator, the method400is returned to block408to proceed forward based on the change in criteria.

The method400may be stopped in block422when the operator is satisfied that the image displayed is the best image for black Polo T-shirt. But the method400may be stopped during any of the steps listed above and may be restarted at any point in the process depending on the actions of the operator.

FIGS. 8 and 9illustrate how an operator can use the UI to choose the best image set from the various images that have been collected from the various sources.FIG. 8shows an initial collection of images which can be displayed to the operator in an operator UI. Using the systems and methods described above, the operator can select a best image set which is then displayed to a customer in the customer UI shown inFIG. 9. The UIs inFIG. 8andFIG. 9can be displayed in any network resource of the system100, such as a web page or as a stand alone display.

Similar to the UIs described above,FIG. 8depicts a UI containing a selected image (and/or image set), but displayed in the context of a web page800. The web page800displays the results of the collection of the images from three different vendors. The vendors (sources1,2, and3) are displayed in the sources section806of the web page800. The web page800also contains a primary image section802where the selected image can be displayed. Additionally, the web page800may contain a textual description area804. If desired, the web page800could also contain any of the fields and components described above.

The primary image section802displays an image (or image set) selected by the image sever116. If desired, the primary image section802may include a hypertext link to further pages containing detailed information about that image that is currently shown. As well, the primary image section802can contain information about the criteria that have been used to select the primary image.

The web page800also contains an additional image section816. The additional section806can show alternate images that can represent the selected image but which, for example, were not selected as the best image. InFIG. 8, the selected image displayed in the primary image section802might show a black boot. The additional image section806may therefore depict alternative images of that black boot or may depict the various secondary images comprising different views (front, back, side, etc. . . . ) of that same boot.

Another part of the web page800contains source section806. The source section806depicts the sources for the collected images. A single source may be displayed or, alternately, a plurality or sources may be shown and optionally associated with the image(s) they have provided. Where a single source has been selected as the preferred source, as described above inFIG. 7, this preference can also be reflected in source section806.

FIG. 8illustrates the various images that may be returned from several different sources (1,2, and3). For example, an image containing a black boot and several other colors of the same boot is shown at810. Additionally, a boot with a higher heel than desired is shown at814and boots of varying color and of different colored backgrounds are shown at812. The items shown at810,812and814each illustrate an image having various features that prevent them from being selected as the best image, e.g., metric and/or coherence problems.

With the information presented in web page800, the operator can access and trigger the image server116. The image server116then operates as described to select the best image from among all of the images displayed on web page800. The best image selected can then be optionally approved by the operator using the UI in web page800.

The best image can then be displayed to the customer by using the UI displayed as part of web page900. Web page900contains the image in primary image section802, as well as image area906which illustrates the best image set containing a complete set of alternate views of the image in section802. Images810,812and814(with undesirable metrics), as well as any duplicate images, have been removed, and are no longer displayed to the customer in web page900. A purchase price has been added to the text portion804and a purchase field has been added for the convenience of the customer.

A limited number of sections are depicted inFIGS. 8 and 9for purposes of clarity. But additional sections or components, including those described above) may be employed as needed for the convenience of the operator or the customer. For example, additional sections could include the metric ranking, a filter section for pre-screening the images, as well as a verification field for the convenience of the operator. As well, depending on the size and format of the device used by the customer, the UI will take various forms and may have more or fewer sections in order to fit the display of that customer device.