Thông tin chung


  Đề tài NC khoa học
  Bài báo, báo cáo khoa học
  Hướng dẫn Sau đại học
  Sách và giáo trình
  Các học phần và môn giảng dạy
  Giải thưởng khoa học, Phát minh, sáng chế
  Khen thưởng
  Thông tin khác

  Tài liệu tham khảo

  Hiệu chỉnh

Số người truy cập: 74,147,226

 A Spatial-pyramid Scene Categorization Algorithm based on Locality-aware Sparse Coding
Tác giả hoặc Nhóm tác giả: Dang Duy Thang, Shintami C. Hidayati, Yung-Yao Chen, Wen-Huang Cheng, Shih-Wei Sun, and Kai-Lung Hua
Nơi đăng: In Proceedings of the IEEE International Conference on Multimedia Big Data (BigMM), Taipei, Taiwan; Số: 93;Từ->đến trang: 342-345;Năm: 2016
Lĩnh vực: Công nghệ thông tin; Loại: Báo cáo; Thể loại: Quốc tế

Scene recognition has a wide range of applications, such as object recognition and detection, content-based image indexing and retrieval and intelligent vehicle and robot navigation. However, the natural scene images tend to be very complex and difficult to analyze due to changes of illumination and transformation. In this study, we will investigate into building a novel model to learn and recognize scenes in nature. This study proposed a new approach that combines locality-constrained sparse coding (LCSP), Spatial Pyramid Pooling and linear SVM in end-to-end model. Firstly, interesting points each image in the training set are extracted by a local descriptor as dense SIFT which represents local spatial information. These features known as codewords and each codeword is represented as part of a topic. Then we employs LCSP algorithm to learn the codeword distribution of those local features from the training dataset. Next, a modified Spatial Pyramid Pooling model is employed for encoding the spatial distribution of local features. Spatial Pyramid Pooling, model has been remarkably successful in terms of both scene and object recognition. In the testing stage, a linear SVM will be used to classify local features which are encoded by Spatial Pyramid Pooling. The new system achieved very competitive results and leading to state-of-the-art performance on several benchmarks.
© Đại học Đà Nẵng
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: