# Unsupervised part learning for visual recognition

## TL;DR

This paper demonstrates that unsupervised learning of discriminative parts from images can enhance visual recognition tasks, outperforming some deep neural network approaches in classification and indexing.

## Contribution

It introduces an unsupervised method for learning discriminative image parts without labeled data, improving classification and retrieval performance.

## Key findings

- Unsupervised parts learning boosts classification accuracy.
- Learned parts outperform holistic CNN features in retrieval tasks.
- Method applicable to category-agnostic applications like image retrieval.

## Abstract

Part-based image classification aims at representing categories by small sets of learned discriminative parts, upon which an image representation is built. Considered as a promising avenue a decade ago, this direction has been neglected since the advent of deep neural networks. In this context, this paper brings two contributions: first, it shows that despite the recent success of end-to-end holistic models, explicit part learning can boosts classification performance. Second, this work proceeds one step further than recent part-based models (PBM), focusing on how to learn parts without using any labeled data. Instead of learning a set of parts per class, as generally done in the PBM literature, the proposed approach both constructs a partition of a given set of images into visually similar groups, and subsequently learn a set of discriminative parts per group in a fully unsupervised fashion. This strategy opens the door to the use of PBM in new applications for which the notion of image categories is irrelevant, such as instance-based image retrieval, for example. We experimentally show that our learned parts can help building efficient image representations, for classification as well as for indexing tasks, resulting in performance superior to holistic state-of-the art Deep Convolutional Neural Networks (DCNN) encoding.