Random maximum margin hashing

Joly, Alexis; Buisson, Olivier

doi:10.1109/cvpr.2011.5995709

Cited by 114 publications

(117 citation statements)

References 19 publications

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“…Here, we aim to evaluate different hashing methods applied to the task of feature fusion, including LDAH (linear discriminant analysis hashing [26]), SSH (semi-supervised hashing [29]), LSH (locality sensitive hashing [5]), SPH (spectral hashing [30]), SVMH (support vector machine hashing [14]), and our RFH (random forest hashing). As shown in Fig.…”

Section: Discussion and Analysismentioning

confidence: 99%

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Learning Compact Binary Codes for Visual Tracking

Shen

Dick

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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A key problem in visual tracking is to represent the appearance of an object in a way that is robust to visual changes. To attain this robustness, increasingly complex models are used to capture appearance variations. However, such models can be difficult to maintain accurately and efficiently. In this paper, we propose a visual tracker in which objects are represented by compact and discriminative binary codes. This representation can be processed very efficiently, and is capable of effectively fusing information from multiple cues. An incremental discriminative learner is then used to construct an appearance model that optimally separates the object from its surrounds. Furthermore, we design a hypergraph propagation method to capture the contextual information on samples, which further improves the tracking accuracy. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.

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Section: Discussion and Analysismentioning

confidence: 99%

“…According to Eq. (14), hypergraph propagation is performed to diffuse the LS-SVM classification scores on the weakly supervised hypergraph, resulting in more accurate object localization. After object localization, we collect some new foreground and background training samples using a spatial sampling scheme [2,33].…”

Section: The Proposed Visual Trackermentioning

confidence: 99%

Learning Compact Binary Codes for Visual Tracking

Shen

Dick

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition

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“…We use a knowledge database, which is off-line indexed using each of the descriptors suggested to the user. On the other hand, the signature of the image sent by the user is computed on-line and a large scale matching algorithm returns the most similar images [3]. A KNN Classifier is then used to build a list of species.…”

Section: Plant Identification Processmentioning

confidence: 99%

An android application for leaf-based plant identification

Mouine

Yahiaoui

Verroust-Blondet

et al. 2013

Proceedings of the 3rd ACM Conference on International Conference on Multimedia Retrieval

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This paper presents an Android application for plant identification. The system relies on the observation of leaf images. Unlike other mobile plant identification applications, t h eu s e rm a yc h o o s et h el e a fc h a r a c t e r st h a tw i l lg u i d et h e identification process. For this purpose, two kinds of descriptors are proposed to the user: a shape descriptor based on a multiscale triangular representation of the leaf margin and a descriptor of the salient points of the leaf. The application achieves good identification accuracy and provides Android users a useful system for plant identification.

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“…Spectral hashing (SH) [7] and anchor graph hashing (AGH) [8], which are formulated based on a graph partitioning problem, are regarded as being an unsupervised learning method. Other unsupervised methods have been proposed such as [9], [10], which construct the binary hash function that uniformly assigns the data to each binary pattern to the extent possible. On the other hand, there are supervised-learning-based methods that use the information of the similar or dissimilar label of pairwise data.…”

Section: Construction Of the Binary Hash Functionmentioning

confidence: 99%

Multiple Binary Codes for Fast Approximate Similarity Search

Shirakawa

2015

IEICE Trans. Inf. & Syst.

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SUMMARYOne of the fast approximate similarity search techniques is a binary hashing method that transforms a real-valued vector into a binary code. The similarity between two binary codes is measured by their Hamming distance. In this method, a hash table is often used when undertaking a constant-time similarity search. The number of accesses to the hash table, however, increases when the number of bits lengthens. In this paper, we consider a method that does not access data with a long Hamming radius by using multiple binary codes. Further, we attempt to integrate the proposed approach and the existing multi-index hashing (MIH) method to accelerate the performance of the similarity search in the Hamming space. Then, we propose a learning method of the binary hash functions for multiple binary codes. We conduct an experiment on similarity search utilizing a dataset of up to 50 million items and show that our proposed method achieves a faster similarity search than that possible with the conventional linear scan and hash table search. key words: similarity search, binary hash, approximate nearest neighbor search, machine learning, image retrieval

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Random maximum margin hashing

Cited by 114 publications

References 19 publications

Learning Compact Binary Codes for Visual Tracking

Learning Compact Binary Codes for Visual Tracking

An android application for leaf-based plant identification

Multiple Binary Codes for Fast Approximate Similarity Search

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