900 research outputs found
Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
The problem of landmark recognition has achieved excellent results in
small-scale datasets. When dealing with large-scale retrieval, issues that were
irrelevant with small amount of data, quickly become fundamental for an
efficient retrieval phase. In particular, computational time needs to be kept
as low as possible, whilst the retrieval accuracy has to be preserved as much
as possible. In this paper we propose a novel multi-index hashing method called
Bag of Indexes (BoI) for Approximate Nearest Neighbors (ANN) search. It allows
to drastically reduce the query time and outperforms the accuracy results
compared to the state-of-the-art methods for large-scale landmark recognition.
It has been demonstrated that this family of algorithms can be applied on
different embedding techniques like VLAD and R-MAC obtaining excellent results
in very short times on different public datasets: Holidays+Flickr1M, Oxford105k
and Paris106k
An Image Indexing and Region based on Color and Texture
From the previous decade, the enormous rise of the internet has tremendously maximized the amount image databases obtainable. This image gathering such as art works, satellite and medicine is fascinating ever more customers in numerous application domains. The work on image retrieval primarily focuses on efficient and effective relevant images from huge and varied image gatherings which is further becoming more fascinating and exciting. In this paper, the author suggested an effective approach for approximating large-scale retrieval of images through indexing. This approach primarily depends on the visual content of the image segment where the segments are obtained through fuzzy segmentation and are demonstrated through high-frequency sub-band wavelets. Furthermore, owing to the complexity in monitoring large scale information and exponential growth of the processing time, approximate nearest neighbor algorithm is employed to enhance the retrieval speed. Thus, a locality-sensitive hashing using (K-NN Algorithm) is adopted for region-aided indexing technique. Particularly, as the performance of K-NN Approach hinges essentially on the hash function segregating the space, a novel function was uncovered motivated using E8 lattice which could efficiently be amalgamated with multiple probes K-NN Approach and query-adaptive K- NN Approach. To validate the adopted hypothetical selections and to enlighten the efficiency of the suggested approach, a group of experimental results associated to the region-based image retrieval is carried out on the COREL data samples
CrossMedia: Supporting Collaborative Research of Media Retrieval
AbstractThe goal of our new e-science platform is to support collaborative research communities by providing a simple solution to jointly develop semantic- and media search algorithms on common and challenging datasets processed by novel feature extractors. Querying of nearest neighbor (NN) elements on large data collections is an important task for several information or content retrieval tasks. In the paper a flexible framework for research purposes is introduced for testing features, metrics, distances and indexing structures. The core part of the content based retrieval system is the LHI-tree, a disk-based index scheme for fast retrieval of multimodal features. Additionally, we compare LHI-tree to FLANN, an effective implementation of ANN search and show that LHI-tree gives similar list of retrieved images
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