[Paper Review] Highly-Economized Multi-View Binary Compression for Scalable Image Clustering

Zheng Zhang, Li Liu+6 more|arXiv (Cornell University)|Sep 17, 2018

Advanced Image and Video Retrieval Techniques43 references4 citations

TL;DR

This paper proposes HSIC, a novel framework for scalable multi-view image clustering that jointly learns compact binary representations and robust discrete cluster structures via unified optimization. By leveraging view-specific and shared information through $ε$-norm constrained discrete optimization, HSIC reduces Euclidean distance computation to efficient XOR operations, achieving state-of-the-art clustering performance with 95% less memory and 90% faster runtime than $k$-means on large-scale datasets like NUS-WIDE (1M features in 81s, 5.52MB memory).

ABSTRACT

How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression. We intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework. In particular, common binary representations are learned by exploiting both sharable and individual information across multiple views to capture their underlying correlations. Meanwhile, cluster assignment with robust binary centroids is also performed via effective discrete optimization under L21-norm constraint. By this means, heavy continuous-valued Euclidean distance computations can be successfully reduced by efficient binary XOR operations during the clustering procedure. To our best knowledge, HSIC is the first binary clustering work specifically designed for scalable multi-view image clustering. Extensive experimental results on four large-scale image datasets show that HSIC consistently outperforms the state-of-the-art approaches, whilst significantly reducing computational time and memory footprint.

Motivation & Objective

• Address the scalability bottleneck of large-scale multi-view image clustering due to high-dimensional real-valued features and expensive Euclidean distance computations.
• Develop a unified framework that jointly optimizes binary representation learning and discrete cluster structure learning for improved efficiency and performance.
• Reduce computational and memory overhead in clustering by replacing continuous-valued operations with fast binary XOR operations via compact binary coding.
• Enable practical deployment on resource-constrained devices by minimizing memory footprint and computational time while maintaining high clustering accuracy.
• Overcome limitations of existing single-view or multi-view clustering methods by integrating both shared and individual information across heterogeneous views in a binary space.

Proposed method

• Proposes a joint optimization framework that simultaneously learns common binary representations and discrete cluster centroids using alternating optimization.
• Employs a $ε$-norm constrained discrete optimization model to learn robust binary cluster centroids, enhancing clustering stability and performance.
• Integrates both sharable and view-specific (individual) information across multiple views into a unified binary code representation to preserve cross-view correlations.
• Replaces expensive real-valued Euclidean distance computations with efficient binary XOR operations by mapping data into a low-dimensional Hamming space.
• Uses an alternating optimization algorithm to solve the joint discrete optimization problem, ensuring high-quality discrete solutions for both binary codes and cluster assignments.
• Applies adaptive weight learning to dynamically balance the importance of different views during binary code learning, improving representation quality.

Experimental results

Research questions

• RQ1 Can a unified framework jointly learning binary representations and discrete cluster structures significantly improve scalability and performance in large-scale multi-view image clustering?
• RQ2 How does the integration of both shared and individual information across multiple heterogeneous views affect clustering performance in the binary space?
• RQ3 To what extent can replacing real-valued Euclidean distance computations with binary XOR operations reduce computational and memory costs without sacrificing clustering accuracy?
• RQ4 How does the proposed method compare to state-of-the-art real-valued and binary clustering methods in terms of accuracy, speed, and memory efficiency on large-scale datasets?
• RQ5 Is the proposed method robust to varying numbers of clusters and code lengths, and can it adaptively determine optimal cluster configurations?

Key findings

• HSIC achieves state-of-the-art clustering performance across four large-scale multi-view datasets, consistently outperforming both real-valued and binary clustering baselines.
• On the NUS-WIDE dataset with 5 views and ~1 million features, HSIC completes clustering in 81 seconds using only 5.52 MB of memory, compared to 29 minutes and 961 MB for $k$-means.
• HSIC reduces memory usage by over 95% and speeds up computation by more than 90% compared to $k$-means, demonstrating strong scalability.
• The method maintains high performance even with short code lengths, with HSIC outperforming $k$-means when code length exceeds 32 bits.
• Ablation studies confirm that removing any component—such as balanced or independence constraints—significantly degrades performance, validating the necessity of the unified design.
• HSIC shows strong adaptability to varying cluster numbers, with optimal performance at 10 clusters on Cifar-10, and consistently ranks among the top three methods across all cluster counts tested.