...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Multimedia Tools and Applications >Hierarchical BoW with segmental sparse coding for large scale image classification and retrieval
【24h】

Hierarchical BoW with segmental sparse coding for large scale image classification and retrieval

机译：带分段稀疏编码的分层BoW用于大规模图像分类和检索

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The bag-of-words (BoW) has been widely regarded as the most successful algorithms for content-based image related tasks, such as large scale image retrieval, classification, and object categorization. Large visual words acquired by BoW quantization through large vocabulary or codebooks have been receiving much attention in the past years. However, not only construction of large vocabulary but also the quantization process impose a heavy burden in terms of time and memory complexities. In order to tackle this issue, we propose an efficient hierarchical BoW (HBoW) to achieve large visual words through quantization by a compact vocabulary instead of large vocabulary. Our vocabulary is very compact since it is only composed of two small dictionaries which is learned through segmental sparse decomposition of local features. To generate the BoW with large size, we first divide the local features into two half parts, and use the two small dictionaries to compute their sparse codes. Then, we map the two indices of the maximum elements of the two sparse codes to a large set of visual words based upon the fact that data with similar properties will share the same base weighted with the largest sparse coefficient. To further make similar patches have higher probability of select the same dictionary base to get similar BoW vectors, we propose a novel collaborative dictionary learning method by imposing the similarity regularization factor together with the row sparsity regularization across data instances during group sparse coding. Additionally, based on index combination of top-2 large sparse codes of local descriptors, we propose a soft BoW assignment method so that our proposed HBoW can tolerate different word selection for similar patches. By employing the inverted file structure built through our HBoW, K-nearest neighbors (KNN) can be efficiently retrieved. After incorporation of our fast KNN search into the SVM-KNN classification method, our HBoW can be used for efficient image classification and logo recognition. Experiments on serval well-known datasets show that our approach is effective for large scale image classification and retrieval.
机译：词袋（BoW）已被广泛认为是基于内容的图像相关任务（例如大规模图像检索，分类和对象分类）的最成功算法。在过去的几年中，通过BoW量化通过大型词汇或密码本获得的大型视觉单词受到了广泛关注。但是，不仅大量词汇的构建而且量化过程在时间和存储器复杂性方面都带来了沉重的负担。为了解决此问题，我们提出了一种有效的层次化BoW（HBoW），可以通过压缩而不是大词汇量的量化词来实现大视觉单词。我们的词汇表非常紧凑，因为它仅由两个小字典组成，这些字典是通过局部特征的分段稀疏分解来学习的。为了生成较大的BoW，我们首先将局部特征分成两个半部分，然后使用这两个小字典来计算其稀疏代码。然后，基于具有相似属性的数据将共享具有最大稀疏系数的相同基本加权这一事实，我们将两个稀疏代码的最大元素的两个索引映射到一大组视觉词。为了进一步使相似的补丁有更高的概率选择相同的词典库来获得相似的BoW向量，我们通过在组稀疏编码期间对数据实例跨所有实例强加相似度正则化因子和行稀疏度正则化，提出了一种新颖的协作词典学习方法。此外，基于局部描述符的前2个大型稀疏码的索引组合，我们提出了一种软BoW分配方法，以便我们提出的HBoW可以容忍相似补丁的不同单词选择。通过使用通过我们的HBoW构建的反向文件结构，可以有效地检索K最近邻居（KNN）。将我们的快速KNN搜索纳入SVM-KNN分类方法后，我们的HBoW可用于有效的图像分类和徽标识别。对服务著名的数据集进行的实验表明，我们的方法对于大规模图像分类和检索是有效的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号