Then out of nowhere she disappeared. I wrote her for her birthday and she replied little. The few that were still my close friends and still hers, told me she had moved on, that it was best not to make contact with her. Then out of the blue some months later she reappeared, but still acting like a douche. Eventually, I decided to give up trying to restore anything with her as she was showing no signs of wanting me back, and started healing.
Around four months after we had ended our relationship, I find out she is with another guy! I could not stand losing her to some other guy, I decided to go diabolical i. I got involved with a lot of fraudulent so-called spell casters on the internet who ripped me off my money without getting a result as to what I wanted. I almost lost my sanity. Just as I almost was giving up, one faithful morning, I received a mail from one of the spell castes I had applied for spell with but never got a reply all along.
He made me to understand that he could not attend to all his costumers then because it was that time of the year for his annual fellowship with his ancestors for the renewal of his spiritual and supernatural gift. I told him not to worry about the spell anymore, that I was done with all of them fake spell casters. I decided to give it a try. After spending about USD which was due to my inability to provide a whole lot of materials which he needed for the spell process , I am happy to announce to the world that I have gotten back my wife and we are expecting our first baby.
All thanks to Dr. Kene Dilli. All you out there tired of all these fraudsters that call themselves spell casters seeking to rip were they have not sown and you require legitimate spell for whatsoever purpose, contact Dr. The results can then be combined to yield a set of metadata weighted in accordance image similarity. Some of the metadata—often including some highly ranked terms—will be of relatively low value in determining image-appropriate responses for presentation to the consumer.
Generally more useful will be metadata descriptors that are relatively unusual. The terms in the weighted metadata list can be further weighted in accordance with their unusualness i. It will be recognized that this set of inferred metadata for the user's cell phone photo was compiled entirely by automated processing of other images, obtained from public sources such as Flickr, in conjunction with other public resources e.
The inferred metadata can naturally be associated with the user's image. More importantly for the present application, however, it can help a service provider decide how best to respond to submission of the user's image. Referring to FIG. As the information is discerned, it can be forwarded by a router to different service providers. These providers may be arranged to handle different types of information e.
Outputs from these service providers are sent to one or more devices e. The present discussion now considers how these service providers decide what responses may be appropriate for a given set of input information.
One approach is to establish a taxonomy of image subjects and corresponding responses. A tree structure can be used, with an image first being classed into one of a few high level groupings e. In use, an image is assessed through different branches of the tree until the limits of available information allow no further progress to be made. Actions associated with the terminal leaf or node of the tree are then taken.
Part of a simple tree structure is shown in FIG. Each node spawns three branches, but this is for illustration only; more or less branches can of course be used. If the subject of the image is inferred to be an item of food e. One starts an online purchase of the depicted item at an online vendor.
The second screen shows nutritional information about the product. The third presents a map of the local area—identifying stores that sell the depicted product. The user switches among these responses using a roller wheel 24 on the side of the phone FIG.
If the subject is inferred to be a photo of a family member or friend, one screen presented to the user gives the option of posting a copy of the photo to the user's FaceBook page, annotated with the person s 's likely name s. Determining the names of persons depicted in a photo can be done by submitting the photo to the user's account at Picasa. Picasa performs facial recognition operations on submitted user images, and correlates facial eigenvectors with individual names provided by the user, thereby compiling a user-specific database of facial recognition information for friends and others depicted in the user's prior images.
Another screen starts a text message to the individual, with the addressing information having been obtained from the user's address book, indexed by the Picasa-determined identity.
The user can pursue any or all of the presented options by switching between the associated screens. If the subject appears to be a stranger e. Such information can be extracted from photos of known persons. VideoSurf is one vendor with a database of facial recognition features for actors and other persons. A still further screen details the degree of separation between the user and the recognized person. Such relationships can be determined from association information published on social networking sites.
Of course, the responsive options contemplated for the different sub-groups of image subjects may meet most user desires, but some users will want something different. One such open-ended approach is to submit the twice-weighted metadata noted above e. Google, per se, is not necessarily best for this function, because current Google searches require that all search terms be found in the results. Better is a search engine that does fuzzy searching, and is responsive to differently-weighted keywords—not all of which need be found.
The results can indicate different seeming relevance, depending on which keywords are found, where they are found, etc. The results from such a search can be clustered by other concepts. Others may be clustered because they concern the works of the architect Raymond Hood.
Others may be clustered as relating to 20 th century American sculpture, or Paul Manship. Information from these clusters can be presented to the user on successive UI screens, e. The order of these screens can be determined by the sizes of the information clusters, or the keyword-determined relevance.
Still a further response is to present to the user a Google search screen—pre-populated with the twice-weighted metadata as search terms.
For example, if little is known about user Ted, but there is a rich set of information available about Ted's friend Alice, that rich set of information may be employed in determining how to respond to Ted, in connection with a given content stimulus. Similarly, if user Ted is a friend of user Alice, and Bob is a friend of Alice, then information relating to Bob may be used in determining an appropriate response to Ted.
The same principles can be employed even if Ted and Alice are strangers, provided there is another basis for implicit trust. While basic profile similarity is one possible basis, a better one is the sharing an unusual attribute or, better, several. Thus, for example, if both Ted and Alice share the traits of being fervent supporters of Dennis Kucinich for president, and being devotees of pickled squid, then information relating to one might be used in determining an appropriate response to present to the other.
The arrangements just-described provides powerful new functionality. They consider the different types of images that may be encountered, and dictate responses or selections of responses that they believe will best satisfy the users' likely desires.
In this respect the above-described arrangements are akin to early indexes of the web—such as Yahoo! Teams of humans generated taxonomies of information for which people might search, and then manually located web resources that could satisfy the different search requests. Eventually the web overwhelmed such manual efforts at organization.
Google's founders were among those that recognized that an untapped wealth of information about the web could be obtained from examining links between the pages, and actions of users in navigating these links. Understanding of the system thus came from data within the system, rather than from an external perspective. Eventually such approaches will be eclipsed by arrangements that rely on machine understanding derived from the system itself, and its use.
One such technique simply examines which responsive screen s are selected by users in particular contexts. As such usage patterns become evident, the most popular responses can be moved earlier in the sequence of screens presented to the user. Likewise, if patterns become evident in use of the open-ended search query option, such action can become a standard response, and moved higher in the presentation queue.
The usage patterns can be tailored in various dimensions of context. Males between 40 and 60 years of age, in New York, may demonstrate interest in different responses following capture of a snapshot of a statue by a 20 th century sculptor, than females between 13 and 16 years of age in Beijing. Most persons snapping a photo of a food processor in the weeks before Christmas may be interested in finding the cheapest online vendor of the product; most persons snapping a photo of the same object the week following Christmas may be interested in listing the item for sale on E-Bay.
Desirably, usage patterns are tracked with as many demographic and other descriptors as possible, so as to be most-predictive of user behavior. More sophisticated techniques can also be applied, drawing from the rich sources of expressly- and inferentially-linked data sources now available.
These include not only the web and personal profile information, but all manner of other digital data we touch and in which we leave traces, e. The network of interrelationships between these data sources is smaller than the network of web links analyzed by Google, but is perhaps richer in the diversity and types of links. From it can be mined a wealth of inferences and insights, which can help inform what a particular user is likely to want done with a particular snapped image.
Artificial intelligence techniques can be applied to the data-mining task. One class of such techniques is natural language processing NLP , a science that has made significant advancements recently. This functionality can be used, e.
The understanding of meaning gained through NLP techniques can also be used to augment image metadata with other relevant descriptors—which can be used as additional metadata in the embodiments detailed herein.
This common sense knowledge can be applied in the metadata processing detailed herein. Wikipedia is another reference source that can serve as the basis for such a knowledge base. Our digital life log is yet another—one that yields insights unique to us as individuals. When applied to our digital life log, NLP techniques can reach nuanced understandings about our historical interests and actions—information that can be used to model predict our present interests and forthcoming actions. This understanding can be used to dynamically decide what information should be presented, or what action should be undertaken, responsive to a particular user capturing a particular image or to other stimulus.
Truly intuitive computing will then have arrived. Indeed, much of the processing of reference data, compilation of glossaries, etc. Flickr, Yahoo! In some embodiments, other processing activities will be started in parallel with those detailed. For example, if initial processing of the first set of reference images suggests that the snapped image is place-centric, the system can request likely-useful information from other resources before processing of the user image is finished.
To illustrate, the system may immediately request a street map of the nearby area, together with a satellite view, a street view, a mass transit map, etc Likewise, a page of information about nearby restaurants can be compiled, together with another page detailing nearby movies and show-times, and a further page with a local weather forecast.
These can all be sent to the user's phone and cached for later display e. These actions can likewise be undertaken before any image processing occurs—simply based on the geocode data accompanying the cell phone image. While geocoding data accompanying the cell phone image was used in the arrangement particularly described, this is not necessary. Other embodiments can select sets of reference images based on other criteria, such as image similarity.
This may be determined by various metrics, as indicated above. Known image classification techniques can also be used to determine one of several classes of images into which the input image falls, so that similarly-classed images can then be retrieved. Another criteria is the IP address from which the input image is uploaded.
Other images uploaded from the same—or geographically-proximate—IP addresses, can be sampled to form the reference sets. Even in the absence of geocode data for the input image, the reference sets of imagery may nonetheless be compiled based on location. Location information for the input image can be inferred from various indirect techniques.
The wireless service provider thru which the cell phone image is relayed may identify the particular cell tower from which the tourist's transmission was received. If the transmission originated through another wireless link, such as WiFi, its location may also be known. The tourist may have used his credit card an hour earlier at a Manhattan hotel, allowing the system with appropriate privacy safeguards to infer that the picture was taken somewhere near Manhattan. Sometimes features depicted in the image are so iconic that a quick search for similar images in Flickr can locate the user e.
GeoPlanet was cited as one source of geographic information. However, a number of other geoinformation databases can alternatively be used. GeoNames-dot-org is one. It will be recognized that archives of aerial imagery are growing exponentially.
Part of such imagery is from a straight-down perspective, but off-axis the imagery increasingly becomes oblique. From two or more different oblique views of a location, a 3D model can be created.
As the resolution of such imagery increases, sufficiently rich sets of data are available that—for some locations—a view of a scene as if taken from ground level may be synthesized. Such views can be matched with street level photos, and metadata from one can augment metadata for the other.
As shown in FIG. These are just a few of the many different information sources that might be employed in such arrangements. Other social networking sites, shopping sites e.
Some of this data reveals information about the user's interests, habits and preferences—data that can be used to better infer the contents of the snapped picture, and to better tailor the intuited response s. Likewise, while FIG. Different interconnections can naturally be employed. The arrangements detailed in this specification are a particular few out of myriad that may be employed. Most embodiments will be different than the ones detailed.
Some actions will be omitted, some will performed in different orders, some will be performed in parallel rather than serially and vice versa , some additional actions may be included, etc. One additional action is to refine the just-detailed process by receiving user-related input, e. The further processing e.
Within an image presented on a touch screen, the user may touch a region to indicate an object of particular relevance within the image frame. Image analysis and subsequent acts can then focus on the identified object. For example, results from one database search can be combined with the original search inputs and used as inputs for a further search. It will be recognized that much of the foregoing processing is fuzzy.
Many of the data is in terms of metrics that have no absolute meaning, but are relevant only to the extent different from other metrics. Many such different probabilistic factors can be assessed and then combined—a statistical stew.
Artisans will recognize that the particular implementation suitable for a given situation may be largely arbitrary. However, thru experience and Bayesian techniques, more informed manners of weighting and using the different factors can be identified and eventually used.
If the Flickr archive is large enough, the first set of images in the arrangement detailed above may be selectively chosen to more likely be similar to the subject image. For example, Flickr can be searched for images taken at about the same time of day. Lighting conditions will be roughly similar, e.
Issues such as seasonal disappearance of the ice skating rink at Rockefeller Center, and snow on a winter landscape, can thus be mitigated. Moreover, the sets of reference images collected from Flickr desirably comprise images from many different sources photographers —so they don't tend towards use of the same metadata descriptors.
Images collected from Flickr may be screened for adequate metadata. For example, images with no metadata except, perhaps, an arbitrary image number may be removed from the reference set s Likewise, images with less than 2 or 20 metadata terms, or without a narrative description, may be disregarded. Flickr is often mentioned in this specification, but other collections of content can of course be used.
Images in Flickr commonly have specified license rights for each image. Systems detailed herein can limit their searches through Flickr for imagery meeting specified license criteria e. Other image collections are in some respect preferable. For example, the database at images google-dot-com seems better at ranking images based on metadata-relevance than Flickr. Flickr and Google maintain image archives that are publicly accessible. Many other image archives are private.
The present technology finds application with both—including some hybrid contexts in which both public and proprietary image collections are used e. Similarly, while reference was made to services such as Flickr for providing data e. One alternative source is an ad hoc peer-to-peer P2P network. In such a P2P arrangement, there may optionally be a central index, with which peers can communicate in searching for desired content, and detailing the content they have available for sharing.
The index may include metadata and metrics for images, together with pointers to the nodes at which the images themselves are stored. The peers may include cameras, PDAs, and other portable devices, from which image information may be available nearly instantly after it has been captured. In the course of the methods detailed herein, certain relationships are discovered between imagery e.
These data are generally reciprocal, so if the system discovers—during processing of Image A, that its color histogram is similar to that of Image B, then this information can be stored for later use. If a later process involves Image B, the earlier-stored information can be consulted to discover that Image A has a similar histogram—without analyzing Image B. Such relationships are akin to virtual links between the images.
For such relationship information to maintain its utility over time, it is desirable that the images be identified in a persistent manner.
If a relationship is discovered while Image A is on a user's PDA, and Image B is on a desktop somewhere, a means should be provided to identify Image A even after it has been transferred to the user's MySpace account, and to track Image B after it has been archived to an anonymous computer in a cloud network. If several different repositories are being searched for imagery or other information, it is often desirable to adapt the query to the particular databases being used.
For example, different facial recognition databases may use different facial recognition parameters. To search across multiple databases, technologies such as detailed in patent applications and can be employed to ensure that each database is probed with an appropriately-tailored query. Frequent reference has been made to images, but in many cases other information may be used in lieu of image information itself.
In different applications image identifiers, characterizing eigenvalues, associated metadata, decoded barcode or watermark data, etc. Location metadata can be used for identifying other resources in addition to similarly-located imagery. Web pages, for example, can have geographical associations e. The web service GeoURL-dot-org is a location-to-URL reverse directory that can be used to identify web sites associated with particular geographies.
Geo Tags. Flickr uses a syntax established by Geobloggers, e. In processing metadata, it is sometimes helpful to clean-up the data prior to analysis, as referenced above.
The metadata may also be examined for dominant language, and if not English or other particular language of the implementation , the metadata and the associated image may be removed from consideration.
Sometimes different analysis engines may be applied to the user's image data. These engines can operate sequentially, or in parallel. For example, FIG. One identifies the person as family, friend or stranger. The other identifies the person as child or adult. The latter two engines work in parallel, after the first has completed its work. Sometimes engines can be employed without any certainty that they are applicable. If the latter engine determines the image is likely a place or thing, the results of the first two engines will likely not be used.
For example, when an image of an aircraft is uploaded to one online site, it returns an identification of the plane by make and model. The arrangements detailed herein can refer imagery that appears to be of aircraft to such a site, and use the returned identification information.
Or all input imagery can be referred to such a site; most of the returned results will be ambiguous and will not be used. Often the different analysis engines and response engines may be operated by different service providers. This consolidation may be performed by the user's cell phone—assembling inputs from different data sources. One example of the technology detailed herein is a homebuilder who takes a cell phone image of a drill that needs a spare part.
Another example is a person shopping for a home. She snaps a photo of the house. The system refers the image both to a private database of MLS information, and a public database such as Google. The system responds with a variety of options, including reviewing photos of the nearest houses offered for sale; reviewing photos of houses listed for sale that are closest in value to the pictured home, and within the same zip-code; reviewing photos of houses listed for sale that are most similar in features to the pictured home, and within the same zip-code; neighborhood and school information, etc.
In another example, a first user snaps an image of Paul Simon at a concert. The system automatically posts the image to the user's Flickr account—together with metadata inferred by the procedures detailed above. The name of the artist may have been found in a search of Google for the user's geolocation; e. The first user's picture, a moment later, is encountered by a system processing a second concert-goer' s photo of the same event, from a different vantage.
The second user is shown the first user's photo as one of the system's responses to the second photo. The system may also alert the first user that another picture of the same event—from a different viewpoint—is available for review on his cell phone, if he'll press a certain button twice. The user can navigate from one to the next—navigating between nodes on a network.
Television shows are rated by the number of viewers, and academic papers are judged by the number of later citations. While Google is limited to analysis and exploitation of links between digital content, the technology detailed herein allows the analysis and exploitation of links between physical content as well and between physical and electronic content. However, the device may be provided with different actuator buttons—each invoking a different operation with the captured image information.
By this arrangement, the user can indicate—at the outset—the type of action intended e. Rather than multiple actuator buttons, the function of a sole actuator button might be controlled in accordance with other UI controls on the device.
For example, repeated pressing of a Function Select button can cause different intended operations to be displayed on the screen of the UI. When the user then presses the shutter button, the selected operation is invoked. One common response which may need no confirmation is to post the image on Flickr or social network site s. Metadata inferred by the processes detailed herein can be saved in conjunction with the imagery qualified, perhaps, as to its confidence. That action identified an X-Y-location coordinate on a virtual landscape e.
Business rules can dictate a response appropriate to a given situation. These rules and responses may be determined by reference to data collected by web indexers, such as Google, etc. Crowdsourcing is not suitable for real-time implementations. However, inputs that stymie the system and fail to yield a corresponding action or yield actions from which user selects none can be referred offline for crowdsource analysis—so that next time it's presented, it can be handled better.
Image-based navigation systems present a different topology than is familiar from web page-based navigation system. For example, web page 1 may link to web pages 2 and 3. Web page 3 may link to page 2. Web page 2 may link to page 4. The individual images are linked a central node e.
Rather, the router takes image information and decides what to do with it, e. Routers can be stand-alone nodes on a network, or they can be integrated with other devices. Or their functionality can be distributed between such locations. A wearable computer may have a router portion e. For example, if it recognizes the image information as being an image of a business card, it may OCR name, phone number, and other data, and enter it into a contacts database. The particular response for different types of input image information can be determined by a registry database, e.
Likewise, while response engines can be stand-alone nodes on a network, they can also be integrated with other devices or their functions distributed.
A wearable computer may have one or several different response engines that take action on information provided by the router portion. The storage or memory can contain content, such as images, audio and video.
Standalone routers and response engines may also be coupled to the network. The computers are networked, shown schematically by link Though the P2P client, computer A may obtain image, video and audio content from computer B. Sharing parameters on computer B can be set to determine which content is shared, and with whom. Data on computer B may specify, for example, that some content is to be kept private; some may be shared with known parties e.
Other information, such as geographic position information, may also be shared—subject to such parameters. In addition to setting sharing parameters based on party, the sharing parameters may also specify sharing based on the content age. In other arrangements, fresher content might be the type most liberally shared.
An exception list can identify content—or one or more classes of content—that is treated differently than the above-detailed rules e. Reference period. People living in the capitals increased by , 1. Capital city growth comprised overseas migration , , natural increase , and internal migration , Melbourne had the largest growth 80, people , Brisbane had the highest growth rate 1. Regional Australia grew by 86, 1.
Life expectancy at birth was There were , registered deaths in , a decrease of 8, since The standardised death rate decreased to 4. Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.
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