A proposal of prior probability-oriented clustering in feature encoding strategies.

Yuki Shinomiya,Yukinobu Hoshino,Ivan Olier

doi:10.1371/journal.pone.0210146

Yuki Shinomiya, Yukinobu Hoshino + Show 1 more

Open Access

https://doi.org/10.1371/journal.pone.0210146

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Journal: PloS one	Publication Date: Jan 10, 2019
License type: CC BY 4.0

Affiliation: Kochi University of Technology

Abstract

Codebook-based feature encodings are a standard framework for image recognition issues. A codebook is usually constructed by clusterings, such as the k-means and the Gaussian Mixture Model (GMM). A codebook size is an important factor to decide the trade-off between recognition performance and computational complexity and a traditional framework has the disadvantage to image recognition issues when a large codebook; the number of unique clusters becomes smaller than a designated codebook size because some clusters converge to close positions. This paper focusses on the disadvantage from a perspective of the distribution of prior probabilities and presents a clustering framework including two objectives that are alternated to the k-means and the GMM. Our approach is first evaluated with synthetic clustering datasets to analyze a difference to traditional clustering. In the experiment section, although our approach alternated to the k-means generates similar results to the k-means results, our approach is able to finely tune clusters for our objective. Our approach alternated to the GMM significantly improves our objective and constructs intuitively appropriate clusters, especially for huge and complicatedly distributed samples. In the experiment on image recognition issues, two state-of-the-art encodings, the Fisher Vector (FV) using the GMM and the Vector of Locally Aggregated Descriptors (VLAD) using the k-means, are evaluated with two publicly available image datasets, the Birds and the Butterflies. For the results of the VLAD with our approach, the recognition performances tend to be worse compared to the original VLAD results. On the other hand, the FV using our approach is able to improve the performance, especially in a larger codebook size.

Highlights

Clustering is a fundamental technique for several purposes such as statistical analysis and data mining
This paper focuses on the codebook construction step and presents a clustering procedure, named prior probability oriented clustering, that generates a suitable codebook considered from the perspective of the distribution of prior probabilities [14] for feature encoding strategies
The rest of this paper is organized as follows: the section briefly reviews the relationship between clustering algorithms and feature encoding approaches; After that, we describe our proposal clustering framework; we analyze numerical characteristics of our proposal with synthetic clustering datasets; After that, we evaluate an effect for image recognition performance with image recognition datasets; we conclude this paper

Summary

Introduction

Clustering is a fundamental technique for several purposes such as statistical analysis and data mining. The main purpose of clustering is to make groups called clusters. Each clustering technique has a specific objective to make groups, such as finding groups that minimize a quantization error and estimation of the appropriate distribution [1, 2]. This paper focusses on clustering in image recognition algorithms and presents an efficient objective. In recent image recognition problems, a local feature framework is a key technique. This detects regions of interest on an image and describes a discriminative feature vector from each

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