Exemplar Based Deep Discriminative and Shareable Feature Learning for Scene Image Classification

In order to encode the class correlation and class specific information in image representation, we propose a new local feature learning approach named Deep Discriminative and Shareable Feature Learning (DDSFL). DDSFL aims to hierarchically learn feature transformation filter banks to transform raw pixel image patches to features. The learned filter banks are expected to: (1) encode common visual patterns of a flexible number of categories; (2) encode discriminative information; and (3) hierarchically extract patterns at different visual levels. Particularly, in each single layer of DDSFL, shareable filters are jointly learned for classes which share the similar patterns. Discriminative power of the filters is achieved by enforcing the features from the same category to be close, while features from different categories to be far away from each other. Furthermore, we also propose two exemplar selection methods to iteratively select training data for more efficient and effective learning. Based on the experimental results, DDSFL can achieve very promising performance, and it also shows great complementary effect to the state-of-the-art Caffe features.Comment: Pattern Recognition, Elsevier, 201

Quantitative Analysis of Economic Complexity and Industrial Competitiveness of Asian Countries

This paper mainly quantifies the economic development situation and industrial competitiveness of Asian countries by measuring the Generalized Economic Complexity Index (GECI) and statistical indicators. The measurement results reveal that it can reflect the real and effective national economic industrial competitiveness more accurately than traditional macro-economic indicators promptly. Another new finding is the GECI of economies, which shows clear geographical differences, with relatively the highest in the East Asia. Besides, we compare the potential of industrial upgrading and conclude that China, Turkey and India have stronger industrial upgrading, while Qatar and Kuwait are obviously weaker

Camera Model Identification with Convolutional Neural Networks and Image Noise Pattern

Camera source detection has drawn a lot of attention in past decade. It enables us to solve a wide range of problems, from crime evidence identification to photo tampering detection. In this paper, some main methods people used in this area for past decade will be reviewed in the Introduction and Method sections. Also in the Method and Result sections, I compared and improved state-of-the-art approaches to solve camera model identification problem. In the latter part, I proposed a novel approaches based on Convolutional Neural Networks and Image Noise Pattern. Results on the dataset from Kaggle shows that to identify source camera, Convolutional Neural Networks can be applied to the pattern noise rather than directly to the original pictures to achieve at least similar or even better performance.Ope

Sentiment Analysis of Steam Review Datasets using Naive Bayes and Decision Tree Classifier

Sentiment analysis or opinion mining is one of the major topics in Natural Language Processing and Text Mining. This paper will provide a complete process of sentiment analysis from data gathering and data preparation to final classification on a user-generated sentimental dataset with Naive Bayes and Decision Tree classifiers. The dataset used for analysis is the product reviews from Steam, a digital distribution platform. The performance of different feature selection models and classifiers will be compared. The trained classifier can be used to make prediction for unlabeled reviews and help companies to increase potential profits in global digital product market.Ope

Nurikabe puzzle

Single-player games (often called puzzles) have received considerable attention from the scientific community. Consequently, interesting insights into some puzzles, and into the approaches for solving them, have emerged. In this article, I focus on Nurikabe puzzle and try to find some pattern of it.Ope