Stmicroelectronics International N.v.

Flytxt B.v.

Samsung Electronics Co., Ltd.

Interra Systems, Inc.

Santanu Chaudhury

Brejesh Lall

Subarna Tripathi

Prateek Kapadia

Jobin Wilson

Mona Mathur

Sumantra Dutta Roy

Saurabh Gorecha

Aditya Khandelia

Shekhar Madnani

Shubhrangam Malaviya

Vinod Vasudevan

Yogesh Singh

Noopur Jain

Shabana K M

Rohit

Abhinav Gupta

Rohit Mungre

A method and system for detection, classification and prediction of user behavior trends using correspondence analysis is disclosed. The methods and systems reduce the n-dimensional feature space to a lower dimensional space for easy processing, improved quality of emerging clusters and superior prediction accuracies. Further, the method applies the correspondence analysis so that each user is assigned with a new coordinate in the lower dimension, which maintains a similarity, difference and the relationship between the variables. Once the correspondence analysis is completed, clustering or grouping of the coordinates based on the similar trends of the users is performed. Further, unlabeled cluster members are assigned class membership proportional to the labeled samples in the cluster. Finally, the method predicts the future actions of the users based on the past trends that are observed from the labeled clusters.

Method and system for automated detection, classification and prediction of multi-scale, multidimensional trends

Systems and methods for management of multi-perspective customer segments. This invention relates to customer management, and more particularly to management of customer segments, wherein the customer segments can be multi-perspective. Embodiments herein disclose methods and systems for generating multiple sets of clusterings of customers based on at least one customer attribute, and combining and interactively visualizing the clusterings, to derive insights.

Systems and methods for management of multi-perspective customer segments

Disclosed herein are methods and systems for providing multivariate time series clustering for customer segmentation. The system comprises of a model management unit that devices a customer segmentation procedure based on temporal variations of user preferences, using MTS clustering, and utilize the discovered clusters to learn association rules specific to each clusters, and improves campaign targeting. The order of the VAR model is fixed based on the nature of the data and length of the time series.

Methods and systems for providing multivariate time series clustering for customer segmentation

A method of providing content is provided. The method includes: receiving, by a target device, first content from a source device; establishing, by the target device, a connection with a cloud computing device; transmitting, by the target device, the first content and content control information to be used to convert the first content, to the cloud computing device; and receiving, by the target device, second content obtained by converting the first content based on the content control information, from the cloud computing device.

Method and apparatus for converting content using cloud

A video compression framework based on parametric object and background compression is proposed. At the encoder, an object is detected and frames are segmented into regions corresponding to the foreground object and the background. The encoder generates object motion and appearance parameters. The motion or warping parameters may include at least two parameters for object translation; two parameters for object scaling in two primary axes and one object orientation parameter indicating a rotation of the object. Particle filtering may be employed to generate the object motion parameters. The proposed methodology is the formalization of the concept and usability for perceptual quality scalability layer for Region(s) of Interest. A coded video sequence format is proposed which aims at 'network friendly' video representation supporting appearance and generalized motion of object(s).

Object tracking

A video compression framework based on parametric object and background compression is proposed. At the encoder, an embodiment detects objects and segments frames into regions corresponding to the foreground object and the background. The object and the background are individually encoded using separate parametric coding techniques. While the object is encoded using the projection of coefficients to the orthonormal basis of the learnt subspace (used for appearance based object tracking), the background is characterized using an auto-regressive (AR) process model. An advantage of the proposed schemes is that the decoder structure allows for simultaneous reconstruction of object and background, thus making it amenable to the new multi-thread/multi-processor architectures.

System and method for object based parametric video coding

Digital video encoder system, method, and non-transitory computer-readable medium for tracking object regions

System and method for identifying erroneous videos and assessing video quality is provided. Feature vectors are generated corresponding to a plurality of frames associated with the one or more videos. The feature vectors are subsequently subjected to anomaly detection to obtain first and second normalized path lengths and normalized anomaly measures. The first and second normalized path lengths and normalized anomaly measures are provided to a regression model to identify the erroneous video.

Visual descriptors based video quality assessment using outlier model

A video compression framework based on parametric object and background compression is proposed. At the encoder, an embodiment detects objects and segments frames into regions corresponding to the foreground object and the background. The object and the background are individually encoded using separate parametric coding techniques. While the object is encoded using the projection coefficients to the orthonormal basis of the learnt subspace (used for appearance based object tracking), the background is characterized using an auto-regressive (AR) process model. An advantage of the proposed schemes is that the decoder structure allows for simultaneous reconstruction of object and background, thus making it amenable to the new multi-thread/multi-processor architectures.

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New Delhi, India

Santanu Chaudhury