Vectors of Locally Aggregated Centers for Compact Video Representation

We propose a novel vector aggregation technique for compact video representation, with application in accurate similarity detection within large video datasets. The current state-of-the-art in visual search is formed by the vector of locally aggregated descriptors (VLAD) of Jegou et. al. VLAD generates compact video representations based on scale-invariant feature transform (SIFT) vectors (extracted per frame) and local feature centers computed over a training set. With the aim to increase robustness to visual distortions, we propose a new approach that operates at a coarser level in the feature representation. We create vectors of locally aggregated centers (VLAC) by first clustering SIFT features to obtain local feature centers (LFCs) and then encoding the latter with respect to given centers of local feature centers (CLFCs), extracted from a training set. The sum-of-differences between the LFCs and the CLFCs are aggregated to generate an extremely-compact video description used for accurate video segment similarity detection. Experimentation using a video dataset, comprising more than 1000 minutes of content from the Open Video Project, shows that VLAC obtains substantial gains in terms of mean Average Precision (mAP) against VLAD and the hyper-pooling method of Douze et. al., under the same compaction factor and the same set of distortions.Comment: Proc. IEEE International Conference on Multimedia and Expo, ICME 2015, Torino, Ital

Rate-Accuracy Trade-Off In Video Classification With Deep Convolutional Neural Networks

Advanced video classification systems decode video frames to derive the necessary texture and motion representations for ingestion and analysis by spatio-temporal deep convolutional neural networks (CNNs). However, when considering visual Internet-of-Things applications, surveillance systems and semantic crawlers of large video repositories, the video capture and the CNN-based semantic analysis parts do not tend to be co-located. This necessitates the transport of compressed video over networks and incurs significant overhead in bandwidth and energy consumption, thereby significantly undermining the deployment potential of such systems. In this paper, we investigate the trade-off between the encoding bitrate and the achievable accuracy of CNN-based video classification models that directly ingest AVC/H.264 and HEVC encoded videos. Instead of retaining entire compressed video bitstreams and applying complex optical flow calculations prior to CNN processing, we only retain motion vector and select texture information at significantly-reduced bitrates and apply no additional processing prior to CNN ingestion. Based on three CNN architectures and two action recognition datasets, we achieve 11%-94% saving in bitrate with marginal effect on classification accuracy. A model-based selection between multiple CNNs increases these savings further, to the point where, if up to 7% loss of accuracy can be tolerated, video classification can take place with as little as 3 kbps for the transport of the required compressed video information to the system implementing the CNN models

Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor

We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams. Our approach begins with the observation that all modern video codecs divide the input frames into macroblocks (MBs). We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a.k.a., MB texture decoding). This in turn allows for the derivation of spatio-temporal video activity regions at extremely high speed in comparison to conventional full-frame decoding followed by optical flow estimation. In order to evaluate the accuracy of a video classification framework based on such activity data, we independently train two CNN architectures on MB texture and MV correspondences and then fuse their scores to derive the final classification of each test video. Evaluation on two standard datasets shows that the proposed approach is competitive to the best two-stream video classification approaches found in the literature. At the same time: (i) a CPU-based realization of our MV extraction is over 977 times faster than GPU-based optical flow methods; (ii) selective decoding is up to 12 times faster than full-frame decoding; (iii) our proposed spatial and temporal CNNs perform inference at 5 to 49 times lower cloud computing cost than the fastest methods from the literature.Comment: Accepted in IEEE Transactions on Circuits and Systems for Video Technology. Extension of ICIP 2017 conference pape

Escaping endpoints explode

In 1988, Mayer proved the remarkable fact that infinity is an explosion point for the set of endpoints of the Julia set of an exponential map that has an attracting fixed point. That is, the set is totally separated (in particular, it does not have any nontrivial connected subsets), but its union with the point at infinity is connected. Answering a question of Schleicher, we extend this result to the set of "escaping endpoints" in the sense of Schleicher and Zimmer, for any exponential map for which the singular value belongs to an attracting or parabolic basin, has a finite orbit, or escapes to infinity under iteration (as well as many other classes of parameters). Furthermore, we extend one direction of the theorem to much greater generality, by proving that the set of escaping endpoints joined with infinity is connected for any transcendental entire function of finite order with bounded singular set. We also discuss corresponding results for *all* endpoints in the case of exponential maps; in order to do so, we establish a version of Thurston's "no wandering triangles" theorem.Comment: 35 pages. To appear in Comput. Methods Funct. Theory. V2: Authors' final accepted manuscript. Revisions and clarifications have been made throughout from V1. This includes improvements in the proof of Proposition 6.11 and Theorem 8.1, as well as corrections in Remarks 7.1 and 7.3 concerning differing definitions of escaping endpoints in greater generalit

Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine

Although many methods have been implemented in the past, face recognition is still an active field of research especially after the current increased interest in security. In this paper, a face recognition system using Kernel Discriminant Analysis (KDA) and Support Vector Machine (SVM) with K-nearest neighbor (KNN) methods is presented. The kernel discriminates analysis is applied for extracting features from input images. Furthermore, SVM and KNN are employed to classify the face image based on the extracted features. This procedure is applied on each of Yale and ORL databases to evaluate the performance of the suggested system. The experimental results show that the system has a high recognition rate with accuracy up to 95.25% on the Yale database and 96% on the ORL, which are considered very good results comparing with other reported face recognition systems

Duration of chronic heart failure affects outcomes with preserved effects of heart rate reduction with ivabradine: findings from SHIFT

Aims: In heart failure (HF) with reduced ejection fraction and sinus rhythm, heart rate reduction with ivabradine reduces the composite incidence of cardiovascular death and HF hospitalization. Methods and results: It is unclear whether the duration of HF prior to therapy independently affects outcomes and whether it modifies the effect of heart rate reduction. In SHIFT, 6505 patients with chronic HF (left ventricular ejection fraction of ≤35%), in sinus rhythm, heart rate of ≥70 b.p.m., treated with guideline-recommended therapies, were randomized to placebo or ivabradine. Outcomes and the treatment effect of ivabradine in patients with different durations of HF were examined. Prior to randomization, 1416 ivabradine and 1459 placebo patients had HF duration of ≥4 weeks and <1.5 years; 836 ivabradine and 806 placebo patients had HF duration of 1.5 years to <4 years, and 989 ivabradine and 999 placebo patients had HF duration of ≥4 years. Patients with longer duration of HF were older (62.5 years vs. 59.0 years; P < 0.0001), had more severe disease (New York Heart Association classes III/IV in 56% vs. 44.9%; P < 0.0001) and greater incidences of co-morbidities [myocardial infarction: 62.9% vs. 49.4% (P < 0.0001); renal dysfunction: 31.5% vs. 21.5% (P < 0.0001); peripheral artery disease: 7.0% vs. 4.8% (P < 0.0001)] compared with patients with a more recent diagnosis. After adjustments, longer HF duration was independently associated with poorer outcome. Effects of ivabradine were independent of HF duration. Conclusions: Duration of HF predicts outcome independently of risk indicators such as higher age, greater severity and more co-morbidities. Heart rate reduction with ivabradine improved outcomes independently of HF duration. Thus, HF treatments should be initiated early and it is important to characterize HF populations according to the chronicity of HF in future trials

Detection of partially overlapped masses in mammograms

Breast cancer remains one of the major causes of cancer deaths among women. For decades, screening mammography has been one of the most common methods for early cancer detection and diagnosis. Digital mammography images are created by applying a small burst of x-rays that pass through the breast to a solid-state detector, which transmits the electronic signals to a computer to form a digital image. However, due to projection, some mass areas may be partially covered, which makes them difficult to be interprated. This paper addresses the issue of potential mass regions being distorted by other normal breast tissues, which will negatively affect some of the features being extracted from the mass and in turn deteriorate the classification accuracy. The goal was to estimate the overlapped parts of the mass border using Euclidean distance in order to give more accurate results in next stages. The presented method achieved 95.744% region sensitivity at 0.333 False Positive per Image (FPI), outperforming other researches in this branch of mammography analysis

Non-escaping endpoints do not explode

The family of exponential maps ƒα(z)=ez + α is of fundamental importance in the study of transcendental dynamics. Here we consider the topological structure of certain subsets of the Julia set J(ƒα). When α ∈ (−∞,−1), and more generally when α belongs to the Fatou set F(ƒα), it is known that J(ƒα) can be written as a union of hairs and endpoints of these hairs. In 1990, Mayer proved for α ∈ (−∞,−1) that, while the set of endpoints is totally separated, its union with infinity is a connected set. Recently, Alhabib and the second author extended this result to the case where α ∈ F(ƒα), and showed that it holds even for the smaller set of all escaping endpoints. We show that, in contrast, the set of non-escaping endpoints together with infinity is totally separated. It turns out that this property is closely related to a topological structure known as a ‘spider’s web’; in particular we give a new topological characterisation of spiders’ webs that maybe of independent interest. We also show how our results can be applied to Fatou’s function, z ↦ z + 1 + e−z

Komparasi Metode Deep Learning, Naïve Bayes Dan Random Forest Untuk Prediksi Penyakit Jantung

Jantung adalah organ yang mempunyai peranan penting dalam kelangsungan hidup manusia karena fungsinya untuk mendistribusikan darah dari paru-paru ke seluruh bagian tubuh, yang dimana darah tersebut mengandung banyak sekali oksigen sehingga dapat membantu proses metabolisme di dalam tubuh manusia. Ada banyak aktivitas dalam tubuh manusia yang tidak dapat diprediksi dalam bentuk umum. Serangan jantung adalah salah satunya, dan itu adalah aktivitas yang sangat serius dalam tubuh manusia yang menyebabkan kematian manusia. Meskipun tidak terlalu terlihat dalam kondisi normal, itu dilakukan secara tiba-tiba. Jadi ini adalah salah satu kejadian yang sangat tidak terduga dalam tubuh manusia. Dengan kemajuan teknologi beberapa algoritma penambangan data dikembangkan untuk memprediksi serangan jantung. Dalam kelanjutannya, algoritma penambangan data yang berbeda, dengan pendekatan machine learning mampu memprediksi terjadinya serangan jantung dalam tubuh manusia. Ini adalah salah satu tugas diagnosis yang khas, tetapi harus dicapai secara akurat dan efisien dengan bantuan pembelajaran mesin. penelitian ini adalah upaya untuk memodelkan dan memecahkan masalah prediksi serangan jantung. Algoritma mesin yang berbeda seperti Deep Learning, Naives Bayes dan Random Forest diambil di sini untuk membentuk model dalam penelitian ini. pendekatan pembelajaran mesin adalah pendekatan yang baik untuk memprediksi terjadinya serangan jantung. Dataset diambil dari laman Kaggle dengan judul heart attack analysis dan prediction dataset. Akurasi paling tinggi yang dapat dihasilkan adalah menggunakan metode algortma deep learning dimana menghasilkan akurasi yang bernilai, yaitu 83,49%