Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders (1703.07980v1)

Abstract: Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft $k$-means scores. At initial stages of the learning procedure, the representations extracted from the auto-encoder may not be very discriminative for latter clustering. We address this issue by adopting a boosted discriminative distribution, where high score assignments are highlighted and low score ones are de-emphasized. With the gradually boosted discrimination, clustering assignment scores are discriminated and cluster purities are enlarged. Experiments on several vision benchmark datasets show that our methods can achieve a state-of-the-art performance.

• The paper introduces an innovative framework that integrates fully convolutional auto-encoders with discriminatively boosted clustering to enhance image clustering.
• It leverages end-to-end feature learning without fully connected layers, preserving spatial locality for more effective representation.
• Experimental results on datasets like MNIST and COIL demonstrate significant gains in accuracy and normalized mutual information.

Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders

The paper introduces a novel image clustering framework leveraging Fully Convolutional Auto-Encoders (FCAE) for image feature extraction and a Discriminatively Boosted Clustering (DBC) technique. The combination of these methods creates an integrated system for simultaneous representation learning and image clustering, addressing traditional challenges in image clustering with distinct strategies.

Methodological Contributions

1. Fully Convolutional Auto-Encoders (FCAE): This approach improves upon conventional stacked auto-encoders by integrating convolutional and de-convolutional layers with max-pooling, unpooling, and batch normalization. The absence of fully connected layers respects spatial locality and exploits images' two-dimensional structural properties. The end-to-end training capability of FCAE circumvents the labor-intensive layer-wise pre-training, efficiently learning features conducive for clustering tasks.
2. Discriminatively Boosted Clustering (DBC): DBC enhances the clustering process by adopting a self-paced learning mechanism, which prioritizes easier, more certain samples initially and progressively incorporates more complex samples. It achieves this by transforming soft k-means scores using a discriminative target distribution to boost high-score assignments. This method enhances the clarity of clustered features and optimizes clustering performance.

Experimental Validation

Extensive experiments on various datasets, including MNIST, USPS, COIL-20, and COIL-100, demonstrate the superiority of the proposed methods over existing clustering frameworks. The results showed significant improvements in clustering accuracy and normalized mutual information, indicating the framework's ability to improve cluster discriminability from ambiguous initial samples.

1. Accuracy (ACC) and Normalized Mutual Information (NMI): The framework consistently outperforms several state-of-the-art clustering techniques, both in terms of ACC and NMI, showcasing its ability to produce higher cluster purity across all tested datasets.
2. FCAE vs. Traditional Methods: When compared to traditional k-means and deep auto-encoder-based methods, FCAE paired with k-means showed notable improvements, underscoring the efficacy of the end-to-end feature learning approach.
3. Unified Clustering Advantage: DBC demonstrates further enhancements over FCAE-based feature extraction by jointly optimizing representation learning and clustering, confirming the synergistic effect of this integration in increasing clustering accuracy.

Theoretical and Practical Implications

The proposed framework bridges the gap between deep feature representation and clustering, yielding enhanced clustering quality. Theoretically, it emphasizes the importance of integrated learning frameworks and represents a significant step forward in feature processing within high-dimensional image spaces. Practically, the framework's applicability extends across various domains requiring image analysis, such as visual concept discovery and automatic image annotation.

Potential Future Directions

Considering the results, potential future research directions could include scaling the FCAE-DBC framework to handle large-scale image datasets like ImageNet and exploring additional constraints to improve clustering results on natural images. The role of certain advanced neural network structures or optimization techniques could also be investigated to further enhance the framework’s performance across more complex and expansive dataset scenarios.

In conclusion, the paper presents a sophisticated method for tackling image clustering by effectively integrating convolutional auto-encoder architectures with a discriminative clustering strategy, achieving state-of-the-art performance on prominent datasets in the field of image processing and analysis.