Ensemble of Part Detectors for Simultaneous Classification and Localization

TL;DR

This paper introduces a novel mid-level representation method using detector responses for simultaneous image classification and object localization, requiring only image-level labels and demonstrating superior performance on benchmarks.

Contribution

It proposes a detector-based spectral clustering for pattern mining and a confidence-loss sparse MIL for detector learning, advancing automatic part discovery without detailed annotations.

Findings

Outperforms existing methods on benchmark datasets.

Effective for both classification and localization tasks.

Utilizes only image-level labels for training.

Abstract

Part-based representation has been proven to be effective for a variety of visual applications. However, automatic discovery of discriminative parts without object/part-level annotations is challenging. This paper proposes a discriminative mid-level representation paradigm based on the responses of a collection of part detectors, which only requires the image-level labels. Towards this goal, we first develop a detector-based spectral clustering method to mine the representative and discriminative mid-level patterns for detector initialization. The advantage of the proposed pattern mining technology is that the distance metric based on detectors only focuses on discriminative details, and a set of such grouped detectors offer an effective way for consistent pattern mining. Relying on the discovered patterns, we further formulate the detector learning process as a confidence-loss sparse Multiple Instance Learning (cls-MIL) task, which considers the diversity of the positive samples, while avoid drifting away the well localized ones by assigning a confidence value to each positive sample. The responses of the learned detectors can form an effective mid-level image representation for both image classification and object localization. Experiments conducted on benchmark datasets demonstrate the superiority of our method over existing approaches.