Deep Binary Reconstruction for Cross-modal Hashing

With the increasing demand of massive multimodal data storage and organization, cross-modal retrieval based on hashing technique has drawn much attention nowadays. It takes the binary codes of one modality as the query to retrieve the relevant hashing codes of another modality. However, the existing binary constraint makes it difficult to find the optimal cross-modal hashing function. Most approaches choose to relax the constraint and perform thresholding strategy on the real-value representation instead of directly solving the original objective. In this paper, we first provide a concrete analysis about the effectiveness of multimodal networks in preserving the inter- and intra-modal consistency. Based on the analysis, we provide a so-called Deep Binary Reconstruction (DBRC) network that can directly learn the binary hashing codes in an unsupervised fashion. The superiority comes from a proposed simple but efficient activation function, named as Adaptive Tanh (ATanh). The ATanh function can adaptively learn the binary codes and be trained via back-propagation. Extensive experiments on three benchmark datasets demonstrate that DBRC outperforms several state-of-the-art methods in both image2text and text2image retrieval task.Comment: 8 pages, 5 figures, accepted by ACM Multimedia 201

Image In-painting Based FMM Algorithm by Edge Prediction Using Gradient Matrix

In this paper, we propose an improved image in-painting method based on Fast Matching Method (FMM) algorithm. The traditional approach speeds less time but it cannot contribute an optimal edge result. To overcome this disadvantage and improve the edge effect. First we use gradient matrix to select less but more significant pixels to join into the gray value calculation. Secondly we use an edge prediction method to predict the edge in the in-painting region and reset the in-painting sequence. Furthermore, this procedure also had an advantage in in-painting the image which had a large destroyed region. Therefore, our improved method contributes an obvious edge for in-painting procedure than the traditional method.The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, Japa

Identify a Specified Fish species by the Co-occurrence and Confusion Matrix

Nowadays, invasive species threaten native species has become a global problem. Invasive species might be carrying pathogenic microorganisms, reduce biological species and even threat to human health. Therefore, in this study, we proposed a method of co-occurrence matrix to texture analysis of three species of fish. We catch the body pattern, and make a judgment based on confusion matrix. Simulation results show that three species of fish can be classified from each other reasonable.The 3rd International Conference on Industrial Application Engineering 2015, March 28-31, 2015, Kitakyushu International Conference Center, Kitakyushu, Japa

Image In-painting Based FMM Algorithm by Edge Prediction Using Gradient Matrix

The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, JapanIn this paper, we propose an improved image in-painting method based on Fast Matching Method (FMM) algorithm. The traditional approach speeds less time but it cannot contribute an optimal edge result. To overcome this disadvantage and improve the edge effect. First we use gradient matrix to select less but more significant pixels to join into the gray value calculation. Secondly we use an edge prediction method to predict the edge in the in-painting region and reset the in-painting sequence. Furthermore, this procedure also had an advantage in in-painting the image which had a large destroyed region. Therefore, our improved method contributes an obvious edge for in-painting procedure than the traditional method