An Effective Color Addition to Feature Detection and Description for Book Spine Image Matching

Spencer G Fowers,Dah-Jye Lee

doi:10.5402/2012/945973

Abstract

The important task of library book inventory, or shelf-reading, requires humans to remove each book from a library shelf, open the front cover, scan a barcode, and reshelve the book. It is a labor-intensive and often error-prone process. Technologies such as 2D barcode scanning or radio frequency identification (RFID) tags have recently been proposed to improve this process. They both incur significant upfront costs and require a large investment of time to fit books with special tags before the system can be productive. A vision-based automation system is proposed to improve this process without those prohibitively high upfront costs. This low-cost shelf-reading system uses a hand-held imaging device such as a smartphone to capture book spine images and a server that processes feature descriptors in these images for book identification. Existing color feature descriptors for feature matching typically use grayscale feature detectors, which omit important color edges. Also, photometric-invariant color feature descriptors require unnecessary computations to provide color descriptor information. This paper presents the development of a simple color enhancement feature descriptor called Color Difference-of-Gaussians SIFT (CDSIFT). CDSIFT is well suited for library inventory process automation, and this paper introduces such a system for this unique application.

Highlights

Taking inventory is a daunting task in any industry, especially when the number of items reaches into the multimillions, as is the case with most major libraries
The evaluation was of color descriptors, a grayscale Difference-of-Gaussian operator was used to detect features
Their results show an increase in performance compared to the original SIFT

Summary

Introduction

Taking inventory is a daunting task in any industry, especially when the number of items reaches into the multimillions, as is the case with most major libraries. Even allocating just one second per book, a full inventory would require over 507 manyears When equipment such as a barcode scanner is used, each book must be taken off the shelf, its cover opened, the barcode scanned, and reshelved. Even with such improved technology, the amount of time and labor required is still substantial. The server system extracts features from the images and calculates descriptors to match features in the captured images to features stored in a central database in order to identify misplaced or missing books It generates a report and a list of actions or graphical instructions for the user to either remove or reshelve the misplaced items.

Results

Background

Algorithms

Implementation

Conclusion