We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

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Going deeper with convolutions

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining - Concepts and Techniques.

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

We propose a new grid-based image representation for discriminative visual object search, with the goal to efficiently locate the query object in a large image collection. After extracting local invariant features, we partition the image into non-overlapping rectangular grid cells. Each grid bundles the local features within it and is characterized by a histogram of visual words. Given both positive and negative queries, each grid is assigned a mutual information score to match and locate the query object. This new image representation offers two great benefits for efficient object search: 1) as the grid bundles local features, the spatial contextual information enhances the discriminative matching; and 2) it enables faster object localization by searching visual object in the grid-level image. To evaluate our approach, we perform experiments on a very challenging logo database BelgaLogos [1] of 10,000 images. The comparison with the state-of-the-art methods highlights the effectiveness of our approach in both accuracy and speed.

/pdf/grid-based-local-feature-bundling-for-efficient-object-l6wd2qo3qs.pdf

Grid-based local feature bundling for efficient object search and localization

Most of the existing co-segmentation methods are usually complex, and require pre-grouping of images, fine-tuning a few parameters and initial segmentation masks etc. These limitations become serious concerns for their application on large scale datasets. In this paper, Group Saliency Propagation (GSP) model is proposed where a single group saliency map is developed, which can be propagated to segment the entire group. In addition, it is also shown how a pool of these group saliency maps can help in quickly segmenting new input images. Experiments demonstrate that the proposed method can achieve competitive performance on several benchmark co-segmentation datasets including ImageNet, with the added advantage of speed up.

Group saliency propagation for large scale and quick image co-segmentation

Anomaly detection is of great interest to big data applications, and both supervised and unsupervised learning have been applied for anomaly detection. However, it still remains a challenging problem because: (1) for supervised learning, it is difficult to acquire training data for anomaly samples; while (2) for unsupervised learning, the performance may not be satisfactory due to the lack of training data. To address the limitations, we propose a hybrid solution by using both normal (positive) data and unlabeled data (could be positive or negative) for semi-supervised anomaly detection. Particularly, we introduce a new framework based on Positive and Unlabeled (PU) Learning using multi-features to detect anomalies. We extend previous PU learning methods to (1) better address unbalanced class problem which is typical for anomaly detection, and (2) handle multiple features for anomaly detection. An iterative algorithm is proposed to learn the anomaly classifier incrementally from the labeled normal data and also unlabeled data. Our proposed method is verified on three benchmark datasets and one synthetic dataset. Experimental results show that our method outperforms existing methods under different class priors and different proportions of given positive classes.

Positive and Unlabeled Learning for Anomaly Detection with Multi-features

Many existing techniques in content based video retrieval treat a video sequence as a whole to match it against a query video or to assign a text label. Such an approach has serious limitations when applied to human action retrieval because an action may occur only in a sub-region and last for a small portion of the video length. In situations like this, we essentially need to match the subvolumes of the video sequences against the query video. A naive exhaustive search is impractical due to large number of possible subvolumes for each video sequence. In this paper, we propose a novel framework for action retrieval which performs pattern matching at subvolume level and is very efficient in handling large corpus of videos. We construct an unsupervised random forest to index the video database, generate a score volume with Hough voting and then employ a max sub-path strategy to quickly search for the temporal and spatial positions of all the video sequences in the database. We present action search experiments on challenging datasets to validate the efficiency and effectiveness of our system.

/pdf/real-time-human-action-search-using-random-forest-based-4n8bh95jrm.pdf

Real-time human action search using random forest based hough voting

Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. A new compact feature descriptor, called Kernel Projection Based SIFT (KPB-SIFT), is presented in this paper. Like SIFT, our descriptor encodes the salient aspects of image information in the feature point's neighborhood. However, instead of using SIFT's smoothed weighted histograms, we apply kernel projection techniques to orientation gradient patches. The produced KPB-SIFT descriptor is more compact as compared to the state-of-the-art, does not require pre-training step needed by PCA based descriptors, and shows superior advantages in terms of distinctiveness, invariance to scale, and tolerance of geometric distortions. We extensively evaluated the effectiveness of KPB-SIFT with datasets acquired under varying circumstances.

Junsong Yuan

Papers

Grid-based local feature bundling for efficient object search and localization

Group saliency propagation for large scale and quick image co-segmentation

Positive and Unlabeled Learning for Anomaly Detection with Multi-features

Real-time human action search using random forest based hough voting

KPB-SIFT: a compact local feature descriptor