Abstract: A method is provided for selecting a query execution plan, including: receiving an XML document including a plurality of root nodes and a plurality of leaf nodes; determining root-to-leaf paths based on the plurality of root nodes and the plurality of leaf nodes; generating a prime number for each unique root-to-leaf path; calculating a number of instances in which each prime number appears; generating an XML synopsis based on the prime numbers and the number of instances; determining a comparison between a query tree pattern and the XML synopsis; determining a type of the query tree pattern; calculating for each query tree pattern plan, a selectivity estimate based on the comparison and the type of the query tree pattern; determining an optimal query execution plan based on the selectivity estimate; and selecting the optimal query execution plan for performing a query of the XML document based on the determination.

Abstract: A system and method for determining a sentiment, a gender and an age group of a subject in a video while the video is being played back. The video is separated into visual data and audio data, the video data is passed to a video processing pipeline and the audio data is passed to both an acoustic processing pipeline and a textual processing pipeline. The system and method performs, in parallel, a video feature extraction process in the video processing pipeline, an acoustic feature extraction process in the acoustic processing pipeline, and a textual feature extraction process in the textual processing pipeline. The system and method combines a resulting visual feature vector, acoustic feature vector, and a textual feature vector into a single feature vector, and determines the sentiment, the gender and the age group of the subject by applying the single feature vector to a machine learning model.

Abstract: Methods, systems, and computer readable media for recognizing one or more hand gestures of a hand-based signal or conversation are described. Some implementations include obtaining one or more unprocessed images of the hand-based signal or conversation including images of at least one of the one or more hand gestures extracting one or more spectral features from the one or more unprocessed images using a Gabor filter bank, receiving the one or more unprocessed images of the one or more hand gestures and the extracted one or more spectral features by a Convolution Neural Network (CNN), and outputting a classification for the at least one of the one or more hand gestures using the Convolution Neural Network (CNN). In some implementations, at least one of the extracted one or more spectral features and at least one of the one or more unprocessed images of the one or more hand gestures are concatenated and input to the Convolution Neural Network (CNN).

Abstract: Methods, systems, and computer readable media for methods, computer readable media, and systems for automatic gender recognition including a phase quantization feature extraction method for automatic gender recognition in smart environments are described.

Abstract: Methods, systems, and computer readable media for recognizing one or more hand gestures of a hand-based signal or conversation are described. Some implementations include obtaining one or more unprocessed images of the hand-based signal or conversation including images of at least one of the one or more hand gestures extracting one or more spectral features from the one or more unprocessed images using a Gabor filter bank, receiving the one or more unprocessed images of the one or more hand gestures and the extracted one or more spectral features by a Convolution Neural Network (CNN), and outputting a classification for the at least one of the one or more hand gestures using the Convolution Neural Network (CNN). In some implementations, at least one of the extracted one or more spectral features and at least one of the one or more unprocessed images of the one or more hand gestures are concatenated and input to the Convolution Neural Network (CNN).

Abstract: A system and method for determining a sentiment, a gender and an age group of a subject in a video while the video is being played back. The video is separated into visual data and audio data, the video data is passed to a video processing pipeline and the audio data is passed to both an acoustic processing pipeline and a textual processing pipeline. The system and method performs, in parallel, a video feature extraction process in the video processing pipeline, an acoustic feature extraction process in the acoustic processing pipeline, and a textual feature extraction process in the textual processing pipeline. The system and method combines a resulting visual feature vector, acoustic feature vector, and a textual feature vector into a single feature vector, and determines the sentiment, the gender and the age group of the subject by applying the single feature vector to a machine learning model.

Abstract: Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.

Abstract: A method for filtering multimodal messages includes receiving an electronic message; enriching the electronic message; extracting one or more features from the electronic message; generating, one or more dendritic cell signals based on the one or more features extracted from the electronic message; subjecting the one or more dendritic cell signals and the electronic message to a dendritic cell algorithm including one or more dendritic cells; determining a maturity of the one or more dendritic cells; and classifying the electronic message as spam based upon the maturity of the one or more dendritic cells.

Abstract: Methods, systems, and computer readable media for methods, computer readable media, and systems for automatic gender recognition including a phase quantization feature extraction method for automatic gender recognition in smart environments are described.

Abstract: Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.

Abstract: Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.

Abstract: Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.

Abstract: Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.

Abstract: Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.

Abstract: Described herein is an apparatus and method for gait recognition. The apparatus includes circuitry that is configured to receive a gait sequence including a predetermined number of image frames of a subject. The received gait sequence is processed to generate a gait-energy-image (GEI). A plurality of Gabor filter responses is computed by convoluting the generated GEI with a bank of Gabor filters, wherein the filter bank includes a first predetermined number of unique scales, and a second predetermined number of unique orientations. Further, the circuitry is configured to partition, each Gabor filter response of the computed plurality of Gabor filter responses, into a predetermined number of overlapping regions and extract, a predetermined number of statistical features only from the overlapping regions, the extracted statistical features corresponding to texture content of the subject. The circuitry eventually recognizes the subject based on a classification of the extracted statistical features.

Abstract: Described herein is an apparatus and method for gait recognition. The apparatus includes circuitry that is configured to receive a gait sequence including a predetermined number of image frames of a subject. The received gait sequence is processed to generate a gait-energy-image (GEI). A plurality of Gabor filter responses is computed by convoluting the generated GEI with a bank of Gabor filters, wherein the filter bank includes a first predetermined number of unique scales, and a second predetermined number of unique orientations. Further, the circuitry is configured to partition, each Gabor filter response of the computed plurality of Gabor filter responses, into a predetermined number of overlapping regions and extract, a predetermined number of statistical features only from the overlapping regions, the extracted statistical features corresponding to texture consent of the subject. The circuitry eventually recognizes the subject based on a classification of the extracted statistical features.

Abstract: Described herein is an apparatus and method for gait recognition. The apparatus includes circuitry that is configured to receive a gait sequence including a predetermined number of image frames of a subject. The received gait sequence is processed to generate a gait-energy-image (GEI). A plurality of Gabor filter responses is computed by convoluting the generated GEI with a bank of Gabor filters, wherein the filter bank includes a first predetermined number of unique scales, and a second predetermined number of unique orientations. Further, the circuitry is configured to partition, each Gabor filter response of the computed plurality of Gabor filter responses, into a predetermined number of overlapping regions and extract, a predetermined number of statistical features only from the overlapping regions, the extracted statistical features corresponding to texture consent of the subject. The circuitry eventually recognizes the subject based on a classification of the extracted statistical features.

Abstract: Described herein is an apparatus and method for gait recognition. The apparatus includes circuitry that is configured to receive a gait sequence including a predetermined number of image frames of a subject. The received gait sequence is processed to generate a gait-energy-image (GEI). A plurality of Gabor filter responses is computed by convoluting the generated GEI with a bank of Gabor filters, wherein the filter bank includes a first predetermined number of unique scales, and a second predetermined number of unique orientations. Further, the circuitry is configured to partition, each Gabor filter response of the computed plurality of Gabor filter responses, into a predetermined number of overlapping regions and extract, a predetermined number of statistical features only from the overlapping regions, the extracted statistical features corresponding to texture content of the subject. The circuitry eventually recognizes the subject based on a classification of the extracted statistical features.

Abstract: Described herein is an apparatus and method for gait recognition. The apparatus includes circuitry that is configured to receive a gait sequence including a predetermined number of image frames of a subject. The received gait sequence is processed to generate a gait-energy-image (GEI). A plurality of Gabor filter responses is computed by convoluting the generated GEI with a bank of Gabor filters, wherein the filter bank includes a first predetermined number of unique scales, and a second predetermined number of unique orientations. Further, the circuitry is configured to partition, each Gabor filter response of the computed plurality of Gabor filter responses, into a predetermined number of overlapping regions and extract, a predetermined number of statistical features only from the overlapping regions, the extracted statistical features corresponding to texture content of the subject. The circuitry eventually recognizes the subject based on a classification of the extracted statistical features.

Abstract: Described herein is an apparatus and method for gait recognition. The apparatus includes circuitry that is configured to receive a gait sequence including a predetermined number of image frames of a subject. The received gait sequence is processed to generate a gait-energy-image (GEI). A plurality of Gabor filter responses is computed by convoluting the generated GEI with a bank of Gabor filters, wherein the filter bank includes a first predetermined number of unique scales, and a second predetermined number of unique orientations. Further, the circuitry is configured to partition, each Gabor filter response of the computed plurality of Gabor filter responses, into a predetermined number of overlapping regions and extract, a predetermined number of statistical features only from the overlapping regions, the extracted statistical features corresponding to texture content of the subject. The circuitry eventually recognizes the subject based on a classification of the extracted statistical features.