# US 2010/0166303 A1 · Object recognition using global similarity-based classifier > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2010/0166303 A1 - Document ID: US20100166303A1 - Status: Published application - Kind: A1 - Application number: 12/347,798 - Filed: 2008-12-31 - Published: 2010-07-01 - Earliest claimed: 2008-12-31 - Inventors: Ali Rahimi - Web page: https://patentgrep.com/patent/US20100166303A1 ## CPC - G06F 18/24133: {Distances to prototypes} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract In some embodiments, object recognition using global similarity-based classifier is presented. In this regard, an apparatus is introduced comprising: a camera, a display, and a processor, the processor to: receive an image from the camera, convert the image to a numerical representation, compute a similarity function between the converted image and a plurality of prototype image representations, and classify the output of the similarity function to identify the image. Other embodiments are also disclosed and claimed. ## Claims ### Claim 1 An apparatus comprising: a camera; a display; and a processor, the processor to: receive an image from the camera; convert the image to a numerical representation; compute a similarity function between the converted image and a plurality of prototype image representations; and classify the output of the similarity function to identify the image. ### Claim 2 (depends on 1) The apparatus of claim 1, further comprising the processor to label the image on the display as being one of the prototype images. ### Claim 3 (depends on 1) The apparatus of claim 1, wherein the processor to compute a similarity function between the converted image and a plurality of prototype image representations comprises the processor to compute maximum weight matchings between sets of SIFT features. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein the processor to compute a similarity function between the converted image and a plurality of prototype image representations comprises the processor to compute Hausdorff distances between edge contour representations. ### Claim 5 (depends on 1) The apparatus of claim 1, wherein the processor to convert the image to a numerical representation comprises the processor to convert the image to color histograms. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein the processor to convert the image to a numerical representation comprises the processor to convert the image to statistics on edge directions. ### Claim 7 A storage medium comprising content which, when executed by an accessing machine, causes the accessing machine to receive an image from a camera, to convert the image to a numerical representation, to compute a similarity function between the converted image and a plurality of prototype image representations, and to classify the output of the similarity function to identify the image. ### Claim 8 (depends on 7) The storage medium of claim 7, further comprising content to label the image on a display as being one of the prototype images. ### Claim 9 (depends on 7) The storage medium of claim 7, wherein the content to compute a similarity function between the converted image and a plurality of prototype image representations comprises content to compute maximum weight matchings between sets of SIFT features. ### Claim 10 (depends on 7) The storage medium of claim 7, wherein the content to compute a similarity function between the converted image and a plurality of prototype image representations comprises content to compute Hausdorff distances between edge contour representations. ### Claim 11 (depends on 7) The storage medium of claim 7, wherein the content to convert the image to a numerical representation comprises content to convert the image to color histograms. ### Claim 12 (depends on 7) The storage medium of claim 7, wherein the content to convert the image to a numerical representation comprises content to convert the image to statistics on edge directions. ### Claim 13 An apparatus comprising: a camera; a display; and a processor, the processor to: receive an image from the camera; convert the image to SIFT features; compute maximum weight matchings between sets of SIFT features of the image and a plurality of prototype images; and classify a fixed-length vector of the maximum weight matchings to identify the image. ### Claim 14 (depends on 13) The apparatus of claim 13, wherein the processor to perform a classification algorithm comprises the processor to perform a nearest neighbors classifier. ### Claim 15 (depends on 13) The apparatus of claim 13, wherein the processor to perform a classification algorithm comprises the processor to perform a k-nearest neighbors classifier. ### Claim 16 (depends on 13) The apparatus of claim 13, wherein the processor to perform a classification algorithm comprises the processor to perform a support vector machine classifier. ### Claim 17 (depends on 13) The apparatus of claim 13, wherein the processor to perform a classification algorithm comprises the processor to perform a decision tree classifier. ### Claim 18 A storage medium comprising content which, when executed by an accessing machine, causes the accessing machine to receive an image from the camera, to convert the image to SIFT features, to compute maximum weight matchings between sets of SIFT features of the image and a plurality of prototype images, and to classify a fixed-length vector of the maximum weight matchings to identify the image. ### Claim 19 (depends on 18) The storage medium of claim 18, wherein the content to perform a classification algorithm comprises content to perform a nearest neighbors classifier. ### Claim 20 (depends on 18) The storage medium of claim 18, wherein the content to perform a classification algorithm comprises content to perform a k-nearest neighbors classifier. ### Claim 21 (depends on 18) The storage medium of claim 18, wherein the content to perform a classification algorithm comprises content to perform a support vector machine classifier. ### Claim 22 (depends on 18) The storage medium of claim 18, wherein the content to perform a classification algorithm comprises content to perform a decision tree classifier.