In feature-based remote sensing image registration, the scale-invariant feature transform (SIFT) algorithm has been one of the most popular solutions. However, it is still a challenge to possess an appropriate amount of correct matches while eliminating mismatches. In this letter, inspired by SIFT, an accurate and robust feature matching framework based on feature slope grouping (FSG) for remote sensing image registration is proposed. Our FSG-SIFT algorithm consists of four major phases: modified SIFT, feature slope computation, feature point grouping, and outlier removal and transformation. Specifically, the random sample consensus is adopted to refine the matches followed by the affine transform. The proposed remote sensing image registration algorithm has been validated on a wide variety of high-resolution orthoimagery data. Experimental results with multispectral and multitemporal images suggested that this new image registration algorithm well improved the feature matching accuracy with better registration performance over five state-of-the-art methods.

We present our evaluation and analysis on Yahoo! Large-scale Flickr-tag Image Classification dataset. Our evaluations show that combining multi-features and different classification models, the MAP of tag prediction can be significantly improve over ordinary linear classification. Further analysis shows that some tags are given not because of the visual content but the meta information of images. Our experiments show that we can make more accurate prediction on certain tags using meta information without any training process, compared with visual content based classifiers. Combine the meta information, multi-features and multi-models fusion, we achieve significantly better performance than simple linear classification. We also evaluate the performance of various mid-level feature, and the results suggest that "Concept Bank" feature may be a promising direction for the task.

Image graph attracts attention from researchers due to the empirical success of graph based semi-supervised learning (SSL) methods and tasks such as image clustering, image navigation. Despite its simple structure, overwhelming scale of online images makes image graph construction a difficult problem. The challenge lies in time-consuming computation, and the difficulty of storing, processing the resulted graphs of huge size. We propose a novel method of image graph construction on MapReduce for large-scale data. The method consists of two stages: the first stage separates images into overlapping groups called image pools by using hash method, and the second computes pairwise similarities for pairs of images that are grouped into common pools. Both stages are performed on MapReduce. Our experiments on large-scale data show that the proposed method generates more sparse image graphs that reserve same or improved accuracy when comparing with previous method.

Recently, several Gaussian like image representations are proposed as an alternative of the bag-of-word representation over local features. These representations are proposed to overcome the quantization error problem faced in bag-of-word representation. They are shown to be effective in different applications, the Extended Hierarchical Gaussianization reached excellent performance using single feature in VOC2009, Vector of Locally Aggregated Descriptors and Fisher Kernel reached excellent performance using only signature like representation on Holiday dataset. Despite their success and similarity, no comparative study about these representations has been made. In this paper, we perform a systematic comparison about three emerging different gaussian like representations: Extended Hierarchical Gaussianization, Fisher Kernel and Vector of Locally Aggregated Descriptors. We evaluate the performance and the influence of feature and parameters of these representations on Holiday and CC_Web_Video datasets, and several important properties about these representations have been observed during our investigation. This study provides better understanding about these gaussian like image representations that are believed to be promising in various applications.

In this paper, we focus on the random forest based concept detection system, and we intend to improve the efficiency of the system in testing phase and to save memory and storage usages by reducing the total number of trees (classifiers). However, reducing the tree number often results in poor performance. In this article, we proposed a method called tree-sharing to cope with this issue. Unlike the traditional method that treats each concept independently, our work shares the trees among concepts, and leave the most important ones from the view of whole system. Experiments on different concept sets show tree-sharing can greatly reduce the number of total trees while the performance decreases slightly. Even in the worst case, we achieve 80% of original performance with only 5% of trees.

We present a scalable mobile video Question-Answering system with locally aggregated descriptors and random projection using user-generated videos all around the world for ACM Multimedia 2011 Technicolor challenge: "precise event recognition and description from video excerpts." Our proposed system takes a video excerpt as a query and explores its canonical semantics of that. We collect a public events video dataset containing 7 topics with 1963 YouTube videos to evaluate our proposed system. The experiment results show that our proposed video feature representation outperforms a state-of-the-art near-duplicate retrieval based on color histogram. The signature generated by random projection not only ensures real-time efficiency but also achieves a competitive MAP performance with original feature.

Semi-supervised learning is to exploit the vast amount of unlabeled data in the world. This paper proposes a scalable graph-based technique leveraging the distributed computing power of the MapReduce programming model. For a higher quality of learning, the paper also presents a multi-layer learning structure to unify both visual and textual information of image data during the learning process. Experimental results show the effectiveness of the proposed methods.

Efficient image search clustering is prominent for image search engines for exponentially growing photo collections. In this work, we propose an image search clustering approach which selects multiple canonical images from image search results and constructs image clusters in real time on an image sub-graph for the search results. The efficiency is achieved with the help of offline-computed image context graphs by distributed computing methods. Extending our prior works, we demonstrate the results of the proposed canonical image selection and preliminary outcomes of large-scale image graph construction in this proposal. We experiment in Flickr550 dataset, containing 540,321 Flickr photos.