PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning

TL;DR

PIGMIL introduces a graph-based method for more accurate positive instance detection in multiple instance learning, leveraging global similarity and robust discrimination to improve detection precision and classifier performance.

Contribution

The paper proposes PIGMIL, a novel graph updating approach that enhances positive instance detection by modeling global similarities and discriminations among instances.

Findings

High precision in TPI detection demonstrated

Outperforms classic baseline MIL methods

Effective in transforming bags into feature vectors

Abstract

Positive instance detection, especially for these in positive bags (true positive instances, TPIs), plays a key role for multiple instance learning (MIL) arising from a specific classification problem only provided with bag (a set of instances) label information. However, most previous MIL methods on this issue ignore the global similarity among positive instances and that negative instances are non-i.i.d., usually resulting in the detection of TPI not precise and sensitive to outliers. To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately. PIGMIL selects instances from working sets (WSs) of some working bags (WBs) as positive candidate pool (PCP). The global similarity among positive instances and the robust discrimination of instances of PCP from negative instances are measured to construct the consistent similarity and discrimination graph (CSDG). As a result, the primary goal (i.e. TPI detection) is transformed into PCP updating, which is approximated efficiently by updating CSDG with a random walk ranking algorithm and an instance updating strategy. At last bags are transformed into feature representation vector based on the identified TPIs to train a classifier. Extensive experiments demonstrate the high precision of PIGMIL's detection of TPIs and its excellent performance compared to classic baseline MIL methods.