Dynamic MLML-tree based adaptive object detection using heterogeneous data distribution

Abstract

We propose a robust object-detector ensemble by introducing a dynamic multi-layer multi-label (MLML)-tree–based adaptive deep learning framework. In many heterogeneous data distributions, deep attributes show latent hierarchical clustering properties. Object detector performance can be enhanced using the dynamic MLML-tree, which can adjust the ambiguities between inter-class nodes and variations between sub-class nodes. In the MLML-tree, dynamic multi-label (DML) trees are configured in two layers and adapt to using a sparse working dataset. First, coarse object clusters are built using an outlier-aware soft-clustering algorithm. Each coarse cluster is denoted by an inter-class node and is associated with an adaptive object detector in DML tree layer 1. It is built by recursively partitioning inter-class nodes until homogeneous object-class leaves are built. DML tree layer 2 is built for each object-class node, which is associated with a convolutional neural network detector, recursively. A novel sub-class can be learned automatically in DML tree layer 2 by applying semi-supervised learning. Extensive experiments show that the proposed method is superior to state-of-the-art techniques using PASCAL Visual Object Classes (VOC) 2007, VOC 2012, and the Microsoft Common Objects in Context (COCO) datasets.