# Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval

## TL;DR

This paper introduces Hierarchy-of-Visual-Words (HoVW), a new hierarchical descriptor for trademark image retrieval that encodes geometric and topological features, improving robustness and performance over previous methods.

## Contribution

The paper proposes a novel hierarchical visual word descriptor for trademark images, enhancing robustness to transformations and outperforming existing retrieval methods.

## Key findings

- HoVW outperforms previous TIR methods on MPEG-7 datasets.
- The hierarchical approach captures geometry and topology effectively.
- Robust against linear and some nonlinear transformations.

## Abstract

In this paper, we present the Hierarchy-of-Visual-Words (HoVW), a novel trademark image retrieval (TIR) method that decomposes images into simpler geometric shapes and defines a descriptor for binary trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical organization of visual data stores each component shape as a visual word. It is capable of representing the geometry of individual elements and the topology of the trademark image, making the descriptor robust against linear as well as to some level of nonlinear transformation. Experiments show that HoVW outperforms previous TIR methods on the MPEG-7 CE-1 and MPEG-7 CE-2 image databases.