Source-linked AI summary
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
Fengfu Li, Hong Qiao, Bo Zhang, Xuanyang Xi
TL;DR
Image clustering traditionally separates feature learning from clustering, while high-dimensional images also require preservation of local structure. The paper introduces an end-to-end FCAE and jointly trained DBC model with boosted assignments, achieving superior performance on visual benchmarks but retaining scale and robustness boundaries.
Problem
Sequential image-clustering pipelines may produce features that are unreliable for clustering, while images present high dimensionality and local structures that clustering should preserve.
Method
The paper combines a fully convolutional auto-encoder with soft k-means in a jointly trained DBC framework using gradually boosted assignment discrimination.
Results
FCAE-based DBC achieves superior performance to analogous methods on several visual datasets, including MNIST, USPS, COIL-20, and COIL-100.
Takeaways & Limitations
Joint representation learning and clustering with boosted discrimination produces image features more suitable for the top clustering categorizer.
Takeaways & Limitations
The authors identify adding constraints for natural images and scaling the algorithm to large datasets such as ImageNet as future work.
Abstract
from arXiv · showhide
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.
1. Introduction
Image clustering must handle high-dimensional images while preserving their local two- and three-dimensional structure. This paper addresses limitations of sequential feature learning and clustering with an end-to-end FCAE-based DBC framework.
- Image clustering seeks compact, object-level models from many unlabeled images for tasks including visual concept discovery, retrieval, and annotation.
- High dimensionality can impair clustering, while image objects contain local two- and three-dimensional structures that should be preserved.
- Sequential pipelines first learn nonlinear embeddings with deep networks and then apply clustering, but the learned features may be unreliable for clustering.
- DBC jointly learns deep image representations and cluster assignments in a self-paced procedure, addressing the separation between feature learning and clustering.
- FCAE enables end-to-end image feature learning with convolutional, de-convolutional, pooling, and un-pooling layers, avoiding layer-wise pre-training.
- FCAE-based DBC outperforms several analogous state-of-the-art methods on MNIST, USPS, COIL-20, and COIL-100 using k-means and deep auto-encoder clustering comparisons.
2. Related work
Related work spans stacked auto-encoders, end-to-end deep auto-encoders, and unified representation-learning and clustering methods. These studies motivate an end-to-end fully convolutional architecture and joint clustering framework.
- Stacked auto-encoders commonly use layer-wise pre-training followed by overall fine-tuning for unsupervised feature extraction and nonlinear dimension reduction.
- Layer-wise pre-training is tedious and time-consuming, especially with Restricted Boltzmann Machine base layers or very deep networks.
- Deep deconvolution networks demonstrated end-to-end training with unpooling and batch normalization, motivating fully convolutional auto-encoders.
- Unified clustering methods such as DEC jointly learn deep representations and cluster assignments, while related approaches combine deep convolutional representations with agglomerative clustering.
3. Proposed methods
The proposed framework jointly learns image representations and cluster assignments using a fully convolutional auto-encoder and soft k-means. Discriminative boosting progressively emphasizes confident assignments so representations become more suitable for clustering.
- Unified framework: DBC combines a fully convolutional auto-encoder with a soft k-means categorizer in a unified image-clustering framework.The auto-encoder provides fast, coarse feature extraction, while the clustering component produces soft assignments and is jointly trained with the encoder.
- Fully convolutional auto-encoder: FCAE is trained end-to-end with convolution, pooling, unpooling, and deconvolution layers while preserving image locality.Its shared convolutional weights use two-dimensional structure and reduce parameter redundancy compared with stacked auto-encoders.
- Discriminatively boosted clustering: The unified model replaces the separately designed auto-encoder preprocessing step with joint feature learning and clustering.The self-paced procedure trusts easier samples first and progressively incorporates harder samples whose assignments span multiple clusters.
- Discriminative target distribution: DBC transforms harder examples into easier ones by boosting high score assignments and reducing low score assignments through a target distribution.The procedure is motivated by progressively focusing on easier instances before more complex objects.
- Learning considerations: A larger boosting factor α accelerates learning but may amplify falsely categorized samples too early, making their features irrecoverable later.The boosting factor controls the speed of the learning process, while normalization can help prevent large clusters from distorting the hidden feature space.
- KL-divergence learning: The joint KL-divergence loss treats ambiguous samples weakly, moderately confident samples seriously, and definitely well-categorized samples as requiring little further feature change.For two clusters, the illustrated loss uses L_ij = r_ij log(r_ij/s_ij).
4. Experiments
The experiments evaluate the proposed methods on several real datasets using benchmark comparisons, visualizations, and ablation studies. The section examines learned features, weights, soft assignments, low-dimensional embeddings, and boosting factors.
- Experimental evaluation: Experiments compare the proposed methods with several state-of-the-art methods on several real datasets.The evaluation includes benchmark results, visualizations, and ablation studies of the learning process.
- Experimental evaluation: The experiments visualize inner features, learned FCAE weights, soft-assignment frequency histograms, and low-dimensional feature embeddings.These visualizations are used alongside quantitative comparisons to inspect the learned representations and clustering process.
- Ablation studies: Ablation studies examine different boosting factors α, normalization methods n_j, and FCAE-based initialization models.These studies target choices affecting the learning process and model initialization.
4.1. Evaluation benchmarks
The evaluation benchmarks compare FCAE and DBC with established clustering methods across handwritten-digit and multi-view object datasets, using ACC and NMI. Results indicate that joint DBC training improves over separate FCAE feature extraction and clustering, especially on digit datasets.
- Datasets: Experiments use MNIST, USPS, COIL-20, and COIL-100, covering handwritten digits and multi-view object images.Dataset categories, image sizes, channels, and sample counts are summarized in Table 1.
- Evaluation metrics: ACC and NMI are used as the two standard evaluation metrics for clustering performance.ACC compares predicted assignments with ground-truth labels, while NMI is based on mutual information and entropy.
- Compared methods: The comparison includes raw-image k-means, auto-encoder-based methods, joint clustering methods, FCAE-KMS, and DBC.DBC jointly trains a unified clustering model, whereas FCAE-KMS applies k-means after FCAE feature extraction.
- Results: On MNIST, DBC outperforms FCAE-KMS and all other compared methods in both ACC and NMI.FCAE features also make k-means competitive with DAE-KMS features, while joint training provides the additional improvement.
- Results: On USPS, DBC improves over FCAE-KMS by a large margin, while on COIL-20 and COIL-100 it achieves only slightly better results.The paper attributes the larger digit-dataset advantage to ambiguous FCAE features and the smaller COIL advantage to already useful FCAE features.
4.2. Visualization
The visualizations examine FCAE activations and filters, DBC assignment-score distributions, embedded feature geometry, and high-confidence errors. Together, they illustrate sparse representations, progressively polarized assignments, improved feature separation, and persistent ambiguity among some digits.
- Inner activations: FCAE activations for MNIST digits 1, 5, and 9 are sparse, while the decoder progressively recovers pooled-map details into a rough image description.The visualization is interpreted as evidence that FCAE retains key information for reconstruction while learning clustering-friendly features.
- Learned filters: The learned FCAE deconvolutional filters are non-trivial Gabor-like shapes produced using max-pooling and unpooling operations.The paper contrasts these filters with localized filters learned by stacked convolutional auto-encoders trained on noisy inputs.
- Assignment-score monitoring: DBC assignment scores evolve from near-uniform values around 0.1 to a two-sided distribution with scores near 0.8 or 0.02.Higher-score samples are progressively emphasized, causing lower-score samples to approach zero for the first cluster.
- Embedded features: t-SNE visualizations show initially overlapping digit features becoming more compact and separated as discriminative boosting proceeds.The hardest displayed distinctions, including 4 versus 9 and 3 versus 5, are mostly well categorized after sufficient boosting epochs.
- False categorizations: The top 100 high-confidence errors reveal persistent confusion among digits 4, 7, and 9, including visually ambiguous handwritten forms.Some 7s contain transverse middle lines, and some images are intrinsically confusing even to human observers.
4.3. Discussions
The ablation studies examine how boosting strength, assignment normalization, and FCAE initialization affect the DBC learning process and performance. Moderate boosting and the proposed normalization are favored, while DBC remains effective across initialization choices.
- Impact of the boosting factor: α = 1.5 makes learning slow, whereas α = 4 accelerates initial learning but can falsely boost ambiguous samples.The larger factor may cause the model to learn too much from false information, reducing performance.
- Impact of the boosting factor: α = 2 produces ACC and NMI curves that grow reasonably and progressively.
- Impact of normalization: Constant normalization can get stuck early, while normalization using original soft assignment scores helps prevent distortion.Initially, the normalization strategies have little effect on ACC and NMI.
- Impact of normalization: The proposed normalization gives the best performance because it directly reflects changes in boosted scores.
- Impact of FCAE initialization: DBC performs greatly with random, half-trained, and sufficiently trained FCAE initialization models.The supplied passage introduces the comparison and reports strong performance across all three initialization settings.
5. Conclusions and future works
The paper proposes FCAE for image representation learning and DBC for image clustering, reporting superior benchmark performance against analogous methods. Future work targets natural-image constraints and scaling to large datasets such as ImageNet.
- Conclusions: FCAE and DBC address image representation learning and image clustering, respectively.
- Conclusions: Benchmarks on several visual datasets show superior performance compared with analogous methods.
- Future works: Future work includes adding suitable FCAE constraints for natural images and scaling the algorithm to large datasets such as ImageNet.
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