IEEE Trans Pattern Anal Mach Intell - Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-scale Image Retrieval.

Tópicos

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Resumo

This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or "classemes" on the ImageNet dataset.

Resumo Limpo

paper address problem learn similaritypreserv binari code effici similar search largescal imag collect formul problem term find rotat zerocent data minim quantiz error map data vertic zerocent binari hypercub propos simpl effici altern minim algorithm accomplish task algorithm dub iter quantiz itq connect multiclass spectral cluster orthogon procrust problem can use unsupervis data embed pca supervis embed canon correl analysi cca result binari code signific outperform sever stateoftheart method also show perform improv can result transform data nonlinear kernel map prior pca cca final demonstr applic itq learn binari attribut classem imagenet dataset

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