大规模图像检索的代码，matlab与c++混合编程。总结了目前图像检索领域目前主要存在的方法。通过阅读该代码，可以对于经典的“词袋”模型（bow模型）有个具体的了解，但是该代码没有提供前序的特征提取，是直接从对提取好的特征向量聚类开始的，包括了k-means，分层k-means（HKM）聚类，倒排文件的建立和索引等，该代码还提供了局部敏感哈希（LSH）方法。最后，这份代码是下面这篇论文的作者提供的， Indexing in Large Scale Image Collections: Scal
We present a novel approach to automatically discover object categories from a collection of unlabeled images. This is achieved by the Information Bottleneck method, which finds the optimal partitioning of the image collection by maximally preservin
This paper is concerned of the loop closure detection problem for visual simultaneous localization and mapping systems.We propose a novel approach based on the stacked denoising auto-encoder (SDA), a multi-layer neural network that autonomously lear
Machine learning is currently a vast area of research with applications in a broad range of fields such as computer vision, bioinformatics, information retrieval, natural language processing, audio processing, data mining, and many others. Among the
Social-Sensed Multimedia Computing Social Embedding Image Distance Learning Reliability of Social Entities Mahanalobis Distance Function Distance Learning with Social Constraints Learning Socially Embedded Visual Representation from Scratch Content-
image matching and recognition with local features
Correspondence
Semi-local and global geometric relations
Ransac and Hough TransformInstance-level recognition
Last time
Local invariant features(last lecture -C. Schmid)
Today
Camera geometry -re
Non-intrusive inspection systerms based on X-ray radiography techriques are rou tinely used at transport hubs to ensure the conforrmity of catgo content with the supplied shipping manifest. As trade volurmes increase and regulatiors become more strin
The pooling step is one of the key components of the well-known Bag-of-visual words (BoW) model widely used in image classification. In this paper, we propose a novel pooling method, which is called Soft-Assignment Location-Orientation Pooling (SALOP
Recently, image representation based on bag-of-visual-words (BoW) model has been popularly applied in image and vision domains. In BoW, a visual codebook of visual words is defined, usually by clustering local features, to represent any novel image w
“视觉词袋”（Bag of Visual Words，BOV）算法是一种有效的基于语义特征表达的物体识别算法。针对传统BOV模型存在的不足，综合利用SAR图像的灰度和纹理特征，提出基于感兴趣目标 （Target of Interest，TOI）的 “视觉词袋”算法。首先，对训练图像进行TOI选取，用灰度共生矩阵模型提取TOI的纹理特征，再结合灰度特征，组成多维特征向量集，以簇内相似度最高、数据分布密度最大为准则，生成“视觉词袋”。其次，对测试图像，依据已生成的“视觉词袋”，采用支持向量机（Sup
