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公开(公告)号：US11568545B2
公开(公告)日：2023-01-31
申请号：US16728714
申请日：2019-12-27
Applicant: A9.com, Inc.
Inventor： R. Manmatha , Hexiang Hu , Deva Ramanan
IPC: G06T7/20 , G06F3/04812 , G06F3/0482 , G06F16/951 , G06T7/174 , G06T7/246 , G06Q30/06
Abstract: Various embodiments of a framework which allow, as an alternative to resource-taxing decompression, efficient computation of feature maps using a compressed content data subset, such as video, by exploiting the motion information, such as a motion vector, present in the compressed video. This framework allows frame-specific object recognition and action detection algorithms to be applied to compressed video and other media files by executing only on I-frames in a Group of Pictures and linearly interpolating the results. Training and machine learning increases recognition accuracy. Yielding significant computational gains, this approach accelerates frame-wise feature extraction I-frame/P-frame/P-frame videos as well as I-frame/P-frame/B-frame videos. The present techniques may also be used for segmentation to identify and label respective regions for objects in a video.
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Patent Agency Ranking
公开(公告)号：US11868440B1
公开(公告)日：2024-01-09
申请号：US16152327
申请日：2018-10-04
Applicant: A9.com, Inc.
Inventor： Yash Patel , R. Manmatha , Alexander Smola , Son D. Tran , Sheng Zha
CPC classification number: G06F18/2193 , G06F16/50 , G06F17/18 , G06F18/2148 , G06N3/04 , G06T1/20
Abstract: Subsets of training data are selected for iterations of a statistical model through a training process. The selection can reduce the amount of data to be processed by selecting the training data that will likely have significant training value for the pass. This can include using a metric such as the loss or certainty to sample the data, such that easy to classify instances are used for training less frequently than harder to classify instances. A cutoff value or threshold can also, or alternatively, be used such that harder to classify instances are not selected for training until later in the process when the model may be more likely to benefit from training on those instances. Sampling can vary between passes for variety, and the cutoff value might also change such that all data instances are eligible for training selection by at least the last iteration.
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公开(公告)号：US10528819B1
公开(公告)日：2020-01-07
申请号：US15818390
申请日：2017-11-20
Applicant: A9.com, Inc.
Inventor： R. Manmatha , Hexiang Hu , Deva Ramanan
Abstract: Various embodiments of a framework which allow, as an alternative to resource-taxing decompression, efficient computation of feature maps using a compressed content data subset, such as video, by exploiting the motion information, such as a motion vector, present in the compressed video. This framework allows frame-specific object recognition and action detection algorithms to be applied to compressed video and other media files by executing only on I-frames in a Group of Pictures and linearly interpolating the results. Training and machine learning increases recognition accuracy. Yielding significant computational gains, this approach accelerates frame-wise feature extraction I-frame/P-frame/P-frame videos as well as I-frame/P-frame/B-frame videos. The present techniques may also be used for segmentation to identify and label respective regions for objects in a video.
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公开(公告)号：US10109051B1
公开(公告)日：2018-10-23
申请号：US15196644
申请日：2016-06-29
Applicant: A9.com, Inc.
Inventor： Aishwarya Natesh , Arnab Sanat Kumar Dhua , Ming Du , R. Manmatha , Colin Jon Taylor , Mehmet Nejat Tek
Abstract: Images may be analyzed to determine a visually cohesive color palette, for example by comparing a subset of the colors most frequently appearing in the image to a plurality of color schemes (e.g., complementary, analogous, etc.), and potentially modifying one or more of the subset of colors to more accurately fit the selected color scheme. Various regions of the image are selected and portions of the regions having one or more colors of the color palette are extracted and classified to generate and compare feature vectors of the patches to previously-determined feature vectors of items to identify visually similar items. The visually similar items are selected for presentation in various ways, such as by choosing an outfit of visually-similar apparel items based on the locations of the corresponding colors in the image, etc.
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公开(公告)号：US20210342924A9
公开(公告)日：2021-11-04
申请号：US16728714
申请日：2019-12-27
Applicant: A9.com, Inc.
Inventor： R. Manmatha , Hexiang Hu , Deva Ramanan
IPC: G06Q30/06 , G06F16/951 , G06F3/0482 , G06F3/0481
Abstract: Various embodiments of a framework which allow, as an alternative to resource-taxing decompression, efficient computation of feature maps using a compressed content data subset, such as video, by exploiting the motion information, such as a motion vector, present in the compressed video. This framework allows frame-specific object recognition and action detection algorithms to be applied to compressed video and other media files by executing only on I-frames in a Group of Pictures and linearly interpolating the results. Training and machine learning increases recognition accuracy. Yielding significant computational gains, this approach accelerates frame-wise feature extraction I-frame/P-frame/P-frame videos as well as I-frame/P-frame/B-frame videos. The present techniques may also be used for segmentation to identify and label respective regions for objects in a video.
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公开(公告)号：US10032072B1
公开(公告)日：2018-07-24
申请号：US15188792
申请日：2016-06-21
Applicant: A9.com, Inc.
Inventor： Son Dinh Tran , R. Manmatha
Abstract: Approaches provide for identifying text represented in image data as well as determining a location or region of the image data that includes the text represented in the image data. For example, a camera of a computing device can be used to capture a live camera view of one or more items. The live camera view can be presented to the user on a display screen of the computing device. An application executing on the computing device or at least in communication with the computing device can analyze the image data of the live camera view to identify text represented in the image data as well as determine locations or regions of the image that include the representations. For example, one such recognition approach includes a region proposal process to generate a plurality of candidate bounding boxes, a region filtering process to determine a subset of the plurality of candidate bounding boxes, a region refining process to refine the bounding box coordinates to more accurately fit the identified text, a text recognizer process to recognize words in the refined bounding boxes, and a post-processing process to suppress overlapping bounding boxes to generate a final set of bounding boxes.
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