Fast On-Line Kernel Density Estimation for Active Object Localization

TL;DR

This paper introduces a fast, online kernel density estimation method for active object localization in images, leveraging context and prior knowledge to improve efficiency and accuracy in complex visual situations.

Contribution

It presents a novel, efficient online kernel-based density estimation technique for dynamic situation modeling in active object localization tasks.

Findings

Efficiently updates situation models using multipole expansion techniques.

Supports accurate object localization in complex, varied datasets.

Demonstrates general applicability to diverse probabilistic machine learning tasks.

Abstract

A major goal of computer vision is to enable computers to interpret visual situations---abstract concepts (e.g., "a person walking a dog," "a crowd waiting for a bus," "a picnic") whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. In this paper, we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images. In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations---a situation model---that represent the expected spatial structure of the given situation. These estimations are efficiently updated by information gained as the system searches for relevant objects, allowing the system to use context as it is discovered to narrow the search. More specifically, at any given time in a run on a test image, our system uses image features plus contextual information it has discovered to identify a small subset of training images---an importance cluster---that is deemed most similar to the given test image, given the context. This subset is used to generate an updated situation model in an on-line fashion, using an efficient multipole expansion technique. As a proof of concept, we apply our algorithm to a highly varied and challenging dataset consisting of instances of a "dog-walking" situation. Our results support the hypothesis that dynamically-rendered, context-based probability models can support efficient object localization in visual situations. Moreover, our approach is general enough to be applied to diverse machine learning paradigms requiring interpretable, probabilistic representations generated from partially observed data.