Context-aware hybrid classification system for fine-grained retail product recognition

Baz, İpek and Yörük, Erdem and Çetin, Müjdat (2016) Context-aware hybrid classification system for fine-grained retail product recognition. In: IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP 2016), Bordeaux, France

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We present a context-aware hybrid classification system for the problem of fine-grained product class recognition in computer vision. Recently, retail product recognition has become an interesting computer vision research topic. We focus on the classification of products on shelves in a store. This 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. The proposed hybrid approach improves the accuracy of context-free image classifiers such as Support Vector Machines (SVMs), by combining them with a probabilistic graphical model such as Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs). The fundamental goal of this paper is using contextual relationships in retail shelves to improve the classification accuracy by executing a context-aware approach.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: CRFs, Context-aware Classification, Probabilistic Graphical Models, HMMs
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Electronics
Faculty of Engineering and Natural Sciences
Depositing User: Müjdat Çetin
Date Deposited: 13 Nov 2016 15:56
Last Modified: 26 Apr 2022 09:24

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