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Content Based Image Retrieval and Its Application to Product Recognition

Abstract : Product Recognition is a challenging problem in many practical applications. This paper presents a new approach for product recognition. By utilizing a set of crawlers our task is to extract informative content from web pages and automatically recognize products found on web pages. A set of images is extracted from each web page and then a new “content-based” image retrieval technique is performed to rank the images from our product catalog. The proposed content-based image retrieval technique utilizes the Empirical Mode Decomposition and processes the first extracted component of the source image. This component maintains the highest local spatial variations of the source image. An adaptive local-threshold technique is applied for the extraction of edges. A quantized and normalized histogram is created for the representation of images. Simulation results reveal that the proposed method is a promising tool for the challenge task of product recognition.
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Submitted on : Friday, October 21, 2016 - 11:36:01 AM
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Petros Fotios Alvanitopoulos, Andrian Moroi, George Bagropoulos, Kieran Dundon. Content Based Image Retrieval and Its Application to Product Recognition. 11th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2015), Sep 2015, Bayonne, France. pp.3-18, ⟨10.1007/978-3-319-23868-5_1⟩. ⟨hal-01385341⟩



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