# Collaborative Descriptors: Convolutional Maps for Preprocessing

## TL;DR

This paper introduces a new preprocessing approach using convolutional maps to enhance hand-crafted features, significantly improving performance in object recognition and car detection tasks.

## Contribution

It proposes a novel collaborative descriptor framework combining deep convolutional maps with traditional features for better image recognition.

## Key findings

- Performance increased by +17.06% in multi-class object recognition.
- Performance increased by +24.71% in car detection.
- Convolutional maps as preprocessing improve feature representation.

## Abstract

The paper presents a novel concept for collaborative descriptors between deeply learned and hand-crafted features. To achieve this concept, we apply convolutional maps for pre-processing, namely the convovlutional maps are used as input of hand-crafted features. We recorded an increase in the performance rate of +17.06 % (multi-class object recognition) and +24.71 % (car detection) from grayscale input to convolutional maps. Although the framework is straight-forward, the concept should be inherited for an improved representation.