title   
  

Retail product recognition with a graphical shelf model (Çizgisel raf modeli ile perakende ürün tanıma)

Baz, İpek and Yörük, Erdem and Çetin, Müjdat (2017) Retail product recognition with a graphical shelf model (Çizgisel raf modeli ile perakende ürün tanıma). In: 25th Signal Processing and Communications Applications Conference (SIU 2017), Antalya, Turkey

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
607Kb

Official URL: http://dx.doi.org/10.1109/SIU.2017.7960546

Abstract

Recently, retail product recognition has become an interesting computer vision research topic. The classification of products on shelves is a very challenging classification problem because many product classes are visually similar in terms of shape, color, texture, and metric size. In shelves, same or similar products are more likely to appear adjacent to each other and displayed in certain arrangements rather than at random. The arrangement of the products on the shelves has a spatial continuity both in brand and metric size. By using this context information, the co-occurrence of the products and the adjacency relations between the products can be statistically modeled. In this work, we present a context-aware hybrid classification system for the problem of fine-grained product class recognition. The proposed hybrid approach improves the accuracy of the context-free image classifiers, by combining them with a probabilistic graphical model based on Hidden Markov Models. The fundamental goal of this paper is to use contextual relationships in retail shelves to improve accuracy of the product classifier.

Item Type:Papers in Conference Proceedings
Subjects:T Technology > TK Electrical engineering. Electronics Nuclear engineering
ID Code:33854
Deposited By:Müjdat Çetin
Deposited On:13 Sep 2017 10:17
Last Modified:13 Sep 2017 10:17

Repository Staff Only: item control page