Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this pa-per we address the new topic of hashing with multi-label data, in which images in the database are assumed to be associated with missing or noisy multiple labels and each query consists of a query image and several textual search terms, similar to the new “Search with Image ” function in-troduced by the Google Image Search. The returned images are judged based on the combination of visual similarity and semantic information conveyed by search terms. In most of the state-of-the-art approaches, the learned hashing func-tions are universal for all labels. To further e...
Abstract—Scalable image search based on visual similarity has been an active topic of research in re...
Hashing techniques have become very popular to solve the content-based image retrieval problem in gi...
© 2017 IEEE. Hashing has been recognized as one of the most promising ways in indexing and retrievin...
Recently, hashing methods have attracted more and more attentions for their effectiveness in large s...
In this paper, most of the existing Hashing methods is mapping the hand extracted features to binary...
Hashing-based approximate nearest-neighbor search may well realize scalable content-based image retr...
This paper proposes a hash function family based on fea-ture vocabularies and investigates the appli...
Representing images by compact hash codes is an attractive approach for large-scale content-based im...
Unsupervised hashing can desirably support scalable content-based image retrieval for its appealing ...
In this thesis we explore methods which learn compact hash coding schemes to encode image databases ...
Similarity search is a key challenge for multimedia retrieval applications where data are usually re...
Hashing compresses high-dimensional features into compact binary codes. It is one of the promising t...
Hashing methods have proven to be effective in the field of large-scale image retrieval. In recent y...
© 2018 IEEE. Unsupervised hashing can desirably support scalable content-based image retrieval for i...
© 1992-2012 IEEE. Hash-based nearest neighbor search has become attractive in many applications. How...
Abstract—Scalable image search based on visual similarity has been an active topic of research in re...
Hashing techniques have become very popular to solve the content-based image retrieval problem in gi...
© 2017 IEEE. Hashing has been recognized as one of the most promising ways in indexing and retrievin...
Recently, hashing methods have attracted more and more attentions for their effectiveness in large s...
In this paper, most of the existing Hashing methods is mapping the hand extracted features to binary...
Hashing-based approximate nearest-neighbor search may well realize scalable content-based image retr...
This paper proposes a hash function family based on fea-ture vocabularies and investigates the appli...
Representing images by compact hash codes is an attractive approach for large-scale content-based im...
Unsupervised hashing can desirably support scalable content-based image retrieval for its appealing ...
In this thesis we explore methods which learn compact hash coding schemes to encode image databases ...
Similarity search is a key challenge for multimedia retrieval applications where data are usually re...
Hashing compresses high-dimensional features into compact binary codes. It is one of the promising t...
Hashing methods have proven to be effective in the field of large-scale image retrieval. In recent y...
© 2018 IEEE. Unsupervised hashing can desirably support scalable content-based image retrieval for i...
© 1992-2012 IEEE. Hash-based nearest neighbor search has become attractive in many applications. How...
Abstract—Scalable image search based on visual similarity has been an active topic of research in re...
Hashing techniques have become very popular to solve the content-based image retrieval problem in gi...
© 2017 IEEE. Hashing has been recognized as one of the most promising ways in indexing and retrievin...