APSIPA Transactions on Signal and Information Processing > Vol 5 > Issue 1

Dimensionality reduction of visual features for efficient retrieval and classification

Industrial Technology Advances

Petros T. Boufounos, MERL – Mitsubishi Electric Research Laboratories, USA, Hassan Mansour, MERL – Mitsubishi Electric Research Laboratories, USA, Shantanu Rane, Palo Alto Research Center (PARC), USA, Anthony Vetro, MERL – Mitsubishi Electric Research Laboratories, USA, avetro@merl.com
 
Suggested Citation
Petros T. Boufounos, Hassan Mansour, Shantanu Rane and Anthony Vetro (2016), "Dimensionality reduction of visual features for efficient retrieval and classification", APSIPA Transactions on Signal and Information Processing: Vol. 5: No. 1, e14. http://dx.doi.org/10.1017/ATSIP.2016.14

Publication Date: 12 Jul 2016
© 2016 Petros T. Boufounos, Hassan Mansour, Shantanu Rane and Anthony Vetro
 
Subjects
 
Keywords
Randomized embeddingsNearest neighborsQuantizationLow-rank matrix factorizationVisual searchClassification
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. QUANTIZED EMBEDDINGS OF FEATURE SPACES 
III. LOW-RANK MATRIX FACTORIZATION OF VISUAL FEATURES 
IV. RELATED WORK 
V. CONCLUDING REMARKS 

Abstract

Visual retrieval and classification are of growing importance for a number of applications, including surveillance, automotive, as well as web and mobile search. To facilitate these processes, features are often computed from images to extract discriminative aspects of the scene, such as structure, texture or color information. Ideally, these features would be robust to changes in perspective, illumination, and other transformations. This paper examines two approaches that employ dimensionality reduction for fast and accurate matching of visual features while also being bandwidth-efficient, scalable, and parallelizable. We focus on two classes of techniques to illustrate the benefits of dimensionality reduction in the context of various industrial applications. The first method is referred to as quantized embeddings, which generates a distance-preserving feature vector with low rate. The second method is a low-rank matrix factorization applied to a sequence of visual features, which exploits the temporal redundancy among feature vectors associated with each frame in a video. Both methods discussed in this paper are also universal in that they do not require prior assumptions about the statistical properties of the signals in the database or the query. Furthermore, they enable the system designer to navigate a rate versus performance trade-off similar to the rate-distortion trade-off in conventional compression.

DOI:10.1017/ATSIP.2016.14