Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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.

Abstract: 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.

Abstract: Disclosed embodiments pertain to a method on a Mobile Station (MS) for input of text for abugida writing systems. In some embodiments, the method may comprise obtaining a base character by performing Optical Character Recognition (OCR) on written user-input on the MS. A conjunct character may also be obtained by applying one or more functional or diacritical operators to the base character. The conjunct character may then be displayed.

Abstract: 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.

Abstract: A method and system for detection, classification and prediction of user behavior trends using correspondence analysis is disclosed. The method and system reduces the n-dimensional feature space to 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.

Abstract: 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.

Abstract: 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.

Abstract: 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).

Abstract: 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).

Abstract: A method, apparatus and system for orienting a disoriented image, and a method, apparatus and system for training a plurality of Gaussian mixture models (GMMs) to orient the disoriented image are provided. The method of training the plurality of GMMs includes: obtaining a plurality of color and texture features from the disoriented image; selecting a plurality of discriminative features from the color and texture features; calculating probabilities of each of the GMMs orienting the disoriented image, where each of the GMMs represents one of a plurality of rotation classes, and each of the rotation classes represents a rotation angle that is a multiple of a right angle. Furthermore, the system includes an electronic device that includes an embedded platform including a processor which processes the disoriented image.