Learning Euclidean Embeddings for Indexing and Classification

Athitsos, Vassilis; Alon, Joni; Sclaroff, Stan; Kollios, George

doi:10.21236/ada461760

Cited by 3 publications

(6 citation statements)

References 24 publications

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“…The data are ordered by increasing differences of distance. It seems to be an efficient method for obtaining a ranking of all objects in approximate order of similarity to the reference object . For the further statistical evaluation the data of these 200 preselected objects are taken.…”

Section: Resultsmentioning

confidence: 99%

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

Gertz¹,

Gertz²,

Matthäus

et al. 2019

Euro J Lipid Sci & Tech

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The verification of the geographical origin of olive oils by analytical techniques is still a challenge. The goal of this work is to explore the application and accuracy of different chemometric tools combined with near infrared spectroscopy (NIR) based analytical methods in the field of geographical authenticity of olive oils. As olive oils associated with different geographical origins are mainly characterized by different fatty acid (FA) and triacylglycerol (TAG) compositions, NIR methods for the fast and reliable determination of these parameters are developed. Next, these NIR methods are used to characterize a comprehensive set of olive oils (n > 5000) derived from 19 different countries. This set of data is used to build a statistical workflow, which allows the determination of the geographical origin of unknown olive oil samples. First of all, the untreated data set is pretreated by k‐means clustering and the selection of the relevant analytical variables by principal component analysis (PCA) and linear discriminant analysis (LDA) and min/max normalization of all parameters. Subsequently, classification is performed with a reduced sample set of the 200 most similar samples identified by k‐nearest neighbor tool (kNN). For classification purpose kNN, LDA, naïve Bayes classifier, and logit regression are applied. Practical Applications: The established statistical workflow can be used to verify the geographical origin of olive oils. The application and usage of up to four different statistical models for classification purpose results in a superior probability of the predicted origin in comparison to the application of only one single statistical classification test. As standardized methods are used as reference methods for building the NIR methods, the FA and TAG composition and the iodine value can be either determined by the standard methods or by the described NIR method. The presented statistical approach will help to build up a system for the verification of the geographical origin of olive oils.

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Section: Resultsmentioning

confidence: 99%

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

Gertz¹,

Gertz²,

Matthäus

et al. 2019

Euro J Lipid Sci & Tech

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“…For BoostMap, all of the training objects were used as candidates for creating the 1D embeddings. We used 200, 000 triplets to train the BoostMap embedding, as suggested in [1]. The number of BoostMap iterations performed was 250.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…First, we briefly review the complexity of BoostMap and RCMap. For BoostMap, each iteration has cost of O(Ct), where C is the number of candidate objects used for creating 1-dimensional embeddings, and t is the number of triples [1]. With i iterations, the total cost of embedding is O(Cti).…”

Section: Running Timementioning

confidence: 99%

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RCMap: Efficiently Creating High-Quality Euclidean Embeddings

Qamra¹,

Chang²

2007

Proceedings of the 2007 SIAM International Conference on Data Mining

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For many applications in computer vision and multimedia, similarity between objects is measured by a dissimilarity function that is complex, expensive to compute, and often non-metric. To allow fast distance computations, these objects may be embedded into a vector space, where the distance between the embedding of two objects approximates the actual dissimilarity between them. Traditional sparse embedding methods like FastMap and SparseMap allow embedding a new object into a vector space based on its dissimilarities with only a small set of objects. However, these methods do not optimize embedding quality, and may create embeddings that do not approximate the original dissimilarities well. BoostMap improves embedding quality, but incurs high computational cost. In this work, we propose RCMap, a technique that offers significant speedup over Boostmap, with minimal loss in embedding quality.

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“…In [1] we provide a more extensive discussion of BoostMap, and we explore some extensions that can improve embedding accuracy and efficiency.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Boostmap: a method for efficient approximate similarity rankings

Athitsos

Alon

Sclaroff

et al.

Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.

118

148

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This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, 1D embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures. In both systems, in quantitative experiments, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings.

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Learning Euclidean Embeddings for Indexing and Classification

Cited by 3 publications

References 24 publications

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

RCMap: Efficiently Creating High-Quality Euclidean Embeddings

Boostmap: a method for efficient approximate similarity rankings

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