Patent US20080159622A1 successfully refreshed from Google Patents.
US20080159622A1
Active / In Force
US
⏱️ 1.1 years remaining
Target object recognition in images and video
Assignee: Nexus Holdings Group LLC
•
Filing: 2007-12-10
•
Granted: —
•
Expires: 2027-12-10
Abstract
Abstract A computer-readable medium for performing target object recognition in images and video includes instructions for receiving target image data including a target object, applying non-negative matrix factorization with enforced sparseness to the target image data to generate target extracted image feature data, training a neural network to identify the target object using the target extracted image feature data to obtain a trained neural network, receiving object image data, applying non-negative matrix factorization with enforced sparseness to the object image data to generate object extracted image feature data, analyzing the object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the object image data, and storing the result of analyzing the object extracted image feature data.
Inventors
- 👤 Naveen Agnihotri
- 👤 Walter Borden
- 👤 David Schieffelin
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-11-09 13:21
CPC Classifications
G; G06; G06V; G06V10/00; G06V10/70; G06V10/82; G06V40/00; G06V40/10; G06V40/16; G06V40/161; G06V10/40; G06V10/44; G06V10/443; G06V10/449; G06V10/451; G06V10/454
Claims Analysis
Total Claims: 24
Claim 1
Independent Claim
190 words
1 . A computer-readable medium comprising instructions, which when executed by a computer system causes the computer system to perform operations for target object recognition in images and video comprising: instructions for receiving an item of target image data, wherein the item of target image data includes a target object; instructions for applying non-negative matrix factorization with enforced sparseness to the item of target image data to generate an item of target extracted image feature data; instructions for training a neural network to identify the target object using the item of target extracted image feature data to obtain a trained neural network; instructions for receiving an item of object image data; instructions for applying non-negative matrix factorization with enforced sparseness to the item of object image data to generate an item of object extracted image feature data; instructions for analyzing the item of object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the item of object image data; and instructions for storing the result of analyzing the item of object extracted image feature data.
Claim 2
Dependent Claim
190 words
1 . A computer-readable medium comprising instructions, which when executed by a computer system causes the computer system to perform operations for target object recognition in images and video comprising: instructions for receiving an item of target image data, wherein the item of target image data includes a target object; instructions for applying non-negative matrix factorization with enforced sparseness to the item of target image data to generate an item of target extracted image feature data; instructions for training a neural network to identify the target object using the item of target extracted image feature data to obtain a trained neural network; instructions for receiving an item of object image data; instructions for applying non-negative matrix factorization with enforced sparseness to the item of object image data to generate an item of object extracted image feature data; instructions for analyzing the item of object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the item of object image data; and instructions for storing the result of analyzing the item of object extracted image feature data.
Claim 3
Dependent Claim
35 words
2 . The computer-readable medium of claim 1 , further comprising instructions for processing the item of target image data with a codex before applying non-negative matrix factorization to the item of target image data.
Claim 4
Dependent Claim
38 words
3 . The computer-readable medium of claim 1 , further comprising instructions for processing an item of object image data with a codex before applying non-negative matrix factorization to the at least on item of object image data.
Claim 5
Dependent Claim
21 words
4 . The computer-readable medium of claim 1 , wherein the item of object image data is received from a webcrawler.
Claim 6
Dependent Claim
21 words
5 . The computer-readable medium of claim 1 , wherein the item of target image data is received via the Internet.
Claim 7
Dependent Claim
39 words
6 . The computer-readable medium of claim 1 , further comprising instructions for adjusting the trained neural network based on a correspondence between a node or weight in the trained neural network and a feature of the target object.
Claim 8
Dependent Claim
199 words
7 . The computer-readable medium of claim 1 , wherein the instructions for applying non-negative matrix factorization with enforced sparseness comprise: instructions for performing factorization of a n×m matrix V including an item of object image data or an item of target image data into non-negative matrices W and H, according to the iterative equations: W ia ← W ia ∑ μ V i μ ( WH ) i μ H a μ W ia ← W ia ∑ j W ja H a μ ← H a μ ∑ j W ia V i μ ( WH ) i μ wherein W is an i×α matrix, H is an α×μ matrix, j is an iterator, and sparseness may be constrained at the end of every iteration according to the equations: sparseness( w i )= S w ,∀i sparseness( h i )= S h ,‡i wherein w i is the i th column of, h i is the i th column of H, and S w and S h are the desired sparseness of W and H.
Claim 9
Dependent Claim
46 words
8 . The computer-readable medium of claim 7 , wherein sparseness is measured using the equation: sparseness ( x ) = n - ( ∑ x i ) / ∑ x i 2 n - 1 wherein n is the dimensionality of x.
Claim 10
Dependent Claim
15 words
9 . The computer-readable medium of claim 1 , wherein the target object is copyrighted.
Claim 11
Dependent Claim
148 words
10 . A computer-implemented method for automated image and object recognition comprising receiving an item of target extracted image feature data generated by applying non-negative matrix factorization with enforced sparseness to an item of object image data, wherein the item of target extracted image feature data includes a target object; training a neural network to identify the target object using the item of target extracted image feature data to obtain a trained neural network; receiving an item of object extracted image feature data generated by applying non-negative matrix factorization with enforced sparseness to an item of object image data; analyzing the item of object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the item of object image data; and storing the result of analyzing the item of object extracted image feature data.
Claim 12
Dependent Claim
148 words
10 . A computer-implemented method for automated image and object recognition comprising receiving an item of target extracted image feature data generated by applying non-negative matrix factorization with enforced sparseness to an item of object image data, wherein the item of target extracted image feature data includes a target object; training a neural network to identify the target object using the item of target extracted image feature data to obtain a trained neural network; receiving an item of object extracted image feature data generated by applying non-negative matrix factorization with enforced sparseness to an item of object image data; analyzing the item of object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the item of object image data; and storing the result of analyzing the item of object extracted image feature data.
Claim 13
Dependent Claim
38 words
11 . The computer-implemented method of claim 10 , further comprising adjusting the trained neural network based on a correspondence between a one node or weight in the trained neural network and a feature of the target object.
Claim 14
Dependent Claim
194 words
12 . The computer-implemented method of claim 10 , wherein applying non-negative matrix factorization with enforced sparseness comprises: performing factorization of a n×m matrix V including an item of object image data or an item of target image data into non-negative matrices W and H, according to the iterative equations: W ia ← W ia ∑ μ V i μ ( WH ) i μ H a μ W ia ← W ia ∑ j W ja H a μ ← H a μ ∑ j W ia V i μ ( WH ) i μ wherein W is an i×α matrix, H is an α×μ matrix, j is an iterator, and sparseness may be constrained at the end of every iteration according to the equations: sparseness( w i )= S w ,∀i sparseness( h i )= S h ,‡i wherein w i is the i th column of, h i is the i th column of H, and S w and S h are the desired sparseness of W and H.
Claim 15
Dependent Claim
46 words
13 . The computer-implemented method of claim 12 , wherein sparseness is measured using the equation: sparseness ( x ) = n - ( ∑ x i ) / ∑ x i 2 n - 1 wherein n is the dimensionality of x.
Claim 16
Dependent Claim
163 words
14 . An apparatus for automated image and object recognition comprising means for receiving an item of target image data, wherein the item of target image data includes a target object; means for applying non-negative matrix factorization with enforced sparseness to the item of target image data to generate an item of target extracted image feature data; means for training a neural network to identify the target object using the target extracted image feature data to obtain a trained neural network; means for receiving an item of object extracted image feature data generated by means for applying non-negative matrix factorization with enforced sparseness to an item of object image data; means for analyzing the item of object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the item of object image data; and means for storing the result of analyzing the item of object extracted image feature data.
Claim 17
Dependent Claim
163 words
14 . An apparatus for automated image and object recognition comprising means for receiving an item of target image data, wherein the item of target image data includes a target object; means for applying non-negative matrix factorization with enforced sparseness to the item of target image data to generate an item of target extracted image feature data; means for training a neural network to identify the target object using the target extracted image feature data to obtain a trained neural network; means for receiving an item of object extracted image feature data generated by means for applying non-negative matrix factorization with enforced sparseness to an item of object image data; means for analyzing the item of object extracted image feature data with the trained neural network to obtain a result indicating whether the presence of the target object is identified in the item of object image data; and means for storing the result of analyzing the item of object extracted image feature data.
Claim 18
Dependent Claim
39 words
15 . The apparatus of claim 14 , further comprising means for adjusting the trained neural network based on a correspondence between a one node or weight in the trained neural network and a feature of the target object.
Claim 19
Dependent Claim
196 words
16 . The apparatus of claim 14 , wherein mean for applying non-negative matrix factorization with enforced sparseness comprises: means for performing factorization of a n×m matrix V including an item of object image data or an item of target image data into non-negative matrices Wand H, according to the iterative equations: W ia ← W ia ∑ μ V i μ ( WH ) i μ H a μ W ia ← W ia ∑ j W ja H a μ ← H a μ ∑ j W ia V i μ ( WH ) i μ wherein W is an i×α matrix, H is an α×μ matrix, j is an iterator, and sparseness may be constrained at the end of every iteration according to the equations: sparseness( w i )= S w ,∀i sparseness( h i )= S h ,‡i wherein w i is the i th column of, h i is the i th column of H, and S w and S h are the desired sparseness of W and H.
Claim 20
Dependent Claim
45 words
17 . The apparatus of claim 16 , wherein sparseness is measured using the equation: sparseness ( x ) = n - ( ∑ x i ) / ∑ x i 2 n - 1 wherein n is the dimensionality of x.
Claim 21
Dependent Claim
128 words
18 . A system for automated image and object recognition a neural network module adapted to receive target extracted image feature data generated by applying non-negative matrix factorization with enforced sparseness to target image data including target extracted image feature data for a target object, be trained to identify the target object with the target extracted image feature data, receive object extracted image feature data generated by applying non-negative matrix factorization with enforced sparseness to object image data, analyze the object extracted image feature data to obtain a result indicating whether the presence of the target object is identified in the object image data, and store the result of analyzing the object extracted image feature data for the presence of the target object in the object image data.
Claim 22
Dependent Claim
128 words
18 . A system for automated image and object recognition a neural network module adapted to receive target extracted image feature data generated by applying non-negative matrix factorization with enforced sparseness to target image data including target extracted image feature data for a target object, be trained to identify the target object with the target extracted image feature data, receive object extracted image feature data generated by applying non-negative matrix factorization with enforced sparseness to object image data, analyze the object extracted image feature data to obtain a result indicating whether the presence of the target object is identified in the object image data, and store the result of analyzing the object extracted image feature data for the presence of the target object in the object image data.
Claim 23
Dependent Claim
168 words
19 . The system of claim 18 , further comprising: a serial target image input device adapted to receive the target image data including the target object, and transmit the target image data to a target image feature extraction device; a serial object image input device adapted to receive the object image data, and transmit the object image data to an object image feature extraction device; the target image feature extraction device adapted to receive the target image data, generate the target extracted image feature data from the target image data by applying non-negative matrix factorization with enforced sparseness to the target image data, and transmit the object extracted image feature data to the neural network module; and the object image feature extraction device adapted receive the object image data, generate the object extracted image feature data from the object image data by applying non-negative matrix factorization with enforced sparseness to the object image data, and transmit the object extracted image feature data to the neural network module.
Claim 24
Dependent Claim
50 words
20 . The system of claim 19 , further comprising an index file storage adapted to receive the object extracted image feature data from the object image feature extraction device, store the object extracted image feature data, and transmit the object extracted image feature data to the neural network module.
References Cited (Backward Citations)
USPTO Annuity & Maintenance Windows
Maintenance schedule is calculated for granted US utility patents.