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—Echocardiography is a widely used and cost-effective medical imaging procedure that is used to diagnose cardiac irregularities. To capture the various chambers of the heart, echocardiography videos are captured from different angles called views to generate standard images/videos. Automatic classification of these views allows for faster diagnosis and analysis. In this work, we propose a representation for echo videos which encapsulates the motion profile of various chambers and valves that helps effective view classification. This variety of motion profiles is captured in a large Gaussian mixture model called universal motion profile model (UMPM). In order to extract only the relevant motion profiles for each view, a factor analysis based decomposition is applied to the means of the UMPM. This results in a low-dimensional representation called motion profile vector (MPV) which captures the distinctive motion signature for a particular view. To evaluate MPVs, a dataset called ECHO 1.0 is introduced which contains around 637 video clips of the four major views: a) parasternal long-axis view (PLAX), b) parasternal short-axis (PSAX), c) apical four-chamber view (A4C), and d) apical two-chamber view (A2C). We demonstrate the efficacy of motion profile-vectors over other spatio-temporal representations. Further, motion profile-vectors can classify even poorly captured videos with high accuracy which shows the robustness of the proposed representation.

A fixed dimensional representation for action clips of varying lengths has been proposed in the literature using aggregation models like bag-of-words and Fisher vector. These representations are high dimensional and require classification techniques for action recognition. In this paper, we propose a framework for unsupervised extraction of a discriminative low-dimensional representation called action-vector. To start with, local spatio-temporal features are utilized to capture the action attributes implicitly in a large Gaussian mixture model called the universal attribute model (UAM). To enhance the contribution of the significant attributes in each action clip, a maximum aposteriori adaptation of the UAM means is performed for each clip. This results in a concatenated mean vector called super action vector (SAV) for each action clip. However, the SAV is still high dimensional because of the presence of redundant attributes. Hence, we employ factor analysis to represent every SAV only in terms of the few important attributes contributing to the action clip. This leads to a low-dimensional representation called action-vector. This entire procedure requires no class labels and produces action-vectors that are distinct representations of each action irrespective of the inter-actor variability encountered in unconstrained videos. An evaluation on trimmed action datasets UCF101 and HMDB51 demonstrates the efficacy of action-vectors for action classification over state-of-the-art techniques. Moreover, we also show that action-vectors can adequately represent untrimmed videos from the THUMOS14 dataset and produce classification results comparable to existing techniques.

Recognition of facial expressions across various actors, contexts, and recording conditions in real-world videos involves identifying local facial movements. Hence, it is important to discover the formation of expressions from local representations captured from different parts of the face. So in this paper, we propose a dynamic kernel-based representation for facial expressions that assimilates facial movements captured using local spatio-temporal representations in a large universal Gaussian mixture model (uGMM). These dynamic kernels are used to preserve local similarities while handling global context changes for the same expression by utilizing the statistics of uGMM. We demonstrate the efficacy of dynamic kernel representation using three different dynamic kernels, namely, explicit mapping based, probability-based, and matching-based, on three standard facial expression datasets, namely, MMI, AFEW, and BP4D. Our evaluations show that probability-based kernels are the most discriminative among the dynamic kernels. However, in terms of computational complexity, intermediate matching kernels are more efficient as compared to the other two representations.

In this paper, an approach for human action recognition using genetic algorithms (GA) and deep convolutional neural networks (CNN) is proposed. We demonstrate that initializing the weights of a convolutional neural network (CNN) classifier based on solutions generated by genetic algorithms (GA) minimizes the classification error. A gradient descent algorithm is used to train the CNN classifiers (to find a local minimum) during fitness evaluations of GA chromosomes. The global search capabilities of genetic algorithms and the local search ability of gradient descent algorithm are exploited to find a solution that is closer to global-optimum. We show that combining the evidences of classifiers generated using genetic algorithms helps to improve the performance. We demonstrate the efficacy of the proposed classification system for human action recognition on UCF50 dataset. HighlightsAn approach for human action recognition using genetic algorithms (GA) and deep convolutional neural networks (CNN) is proposed.The global and local search capabilities of genetic algorithms and gradient descent algorithms, respectively, are exploited by initializing the CNN classifier with the solutions generated by genetic algorithms and training the classifiers using gradient descent algorithm for fitness evaluation of GA chromosomes.Also, the evolution of candidate solutions explored by GA framework is examined.A near accurate recognition performance of 99.98

Actions can be recognized effectively when the various atomic attributes forming the action are identified and combined in the form of a representation. In this paper, a low-dimensional representation is extracted from a pool of attributes learned in a universal Gaussian mixture model using factor analysis. However, such a representation cannot adequately discriminate between actions with similar attributes. Hence, we propose to classify such actions by leveraging the corresponding class labels. We train a Siamese deep neural network with a contrastive loss on the low-dimensional representation. We show that Siamese networks allow effective discrimination even between similar actions. The efficacy of the proposed approach is demonstrated on two benchmark action datasets, HMDB51 and MPII Cooking Activities. On both the datasets, the proposed method improves the state-of-the-art performance considerably.

Representation and modelling of movements play a significant role in recognising actions in unconstrained videos. However, explicit segmentation and labelling of movements are non-trivial because of the variability associated with actors, camera viewpoints, duration etc. Therefore, we propose to train a GMM with a large number of components termed as a universal movement model (UMM). This UMM is trained using motion boundary histograms (MBH) which capture the motion trajectories associated with the movements across all possible actions. For a particular action video, the MAP adapted mean vectors of the UMM are concatenated to form a fixed dimensional representation referred to as “super movement vector” (SMV). However, SMV is still high dimensional and hence, Baum-Welch statistics extracted from the UMM are used to arrive at a compact representation for each action video, which we refer to as an “action-vector”. It is shown that even without the use of class labels, action-vectors provide a more discriminatory representation of action classes translating to a 8 % relative improvement in classification accuracy for action-vectors based on MBH features over naive MBH features on the UCF101 dataset. Furthermore, action-vectors projected with LDA achieve 93% accuracy on the UCF101 dataset which rivals state-of-the-art deep learning techniques.

Feature descriptors involved in video processing are generally high dimensional in nature. Even though the extracted features are high dimensional, many a times the task at hand depends only on a small subset of these features. For example, if two actions like running and walking have to be identified, extracting features related to the leg movement of the person is enough. Since, this subset is not known apriori, we tend to use all the features, irrespective of the complexity of the task at hand. Selecting task-aware features may not only improve the efficiency but also the accuracy of the system. In this work, we propose a supervised approach for task-aware selection of features using Deep Neural Networks (DNN) in the context of action recognition. The activation potentials contributed by each of the individual input dimensions at the first hidden layer are used for selecting the most appropriate features. The selected features are found to give better classification performance than the original high-dimensional features. It is also shown that the classification performance of the proposed feature selection technique is superior to the low-dimensional representation obtained by principal component analysis (PCA).

De-duplication of biometrics is not scalable when the number of people to be enrolled into the biometric system runs into billions, while creating a unique identity for every person. In this paper, we propose an iris classification based on sparse representation of log-gabor wavelet features using on-line dictionary learning (ODL) for large-scale de-duplication applications. Three different iris classes based on iris fiber structures, namely, stream, flower, jewel and shaker, are used for faster retrieval of identities. Also, an iris adjudication process is illustrated by comparing the matched iris-pair images side-by-side to make the decision on the identification score using color coding. Iris classification and adjudication are included in iris de-duplication architecture to speed-up the identification process and to reduce the identification errors. The efficacy of the proposed classification approach is demonstrated on the standard iris database, UPOL.