Recognizing Groceries in situ Using in vitro Training Data

Abstract

The problem of using pictures of objects captured under ideal imaging conditions (here referred to as in vitro) to recognize objects in natural environments (in situ) is an emerging area of interest in computer vision and pattern recognition. Examples of tasks in this vein include assistive vision systems for the blind and object recognition for mobile robots; the proliferation of image databases on the web is bound to lead to more examples in the near future. Despite its importance, there is still a need for a freely available database to facilitate study of this kind of training/testing dichotomy. In this work one of our contributions is a new multimedia database of 120 grocery products, GroZi-120. For every product, two different recordings are available: in vitro images extracted from the web, and in situ images extracted from camcorder video collected inside a grocery store. As an additional contribution, we present the results of applying three commonly used object recognition/detection algorithms (color histogram matching, SIFT matching, and boosted Haar-like features) to the dataset. Finally, we analyze the successes and failures of these algorithms against product type and imaging conditions, both in terms of recognition rate and localization accuracy, in order to suggest ways forward for further research in this domain.

Accessible Shopping Systems for Blind and Visually Impaired Individuals: Design Requirements and the State of the Art

“…GroZi [17] is an accessible shopping project at UC San Diego. The project has three components: 1) an accessible web site for blind and VI users to create grocery shopping lists in the comfort of their homes; 2) computer vision software for recognizing products and signs in stores; and 3) portable devices that can execute computer vision algorithms and give the user haptic and verbal feedback.…”

CAPTCHA-based image labeling on the Soylent Grid

“…For our experiments, we used the GROZI-120 data set, which has been introduced in [1]. It consists of product images for 120 grocery products, which are completely labeled and can therefore be used either for experiment or control data sets.…”

Logo Matching for Document Image Retrieval Using SIFT Descriptors

“…We first extract the object contours from the edge image computed by the Canny edge detector [4] and fill in the gaps along the contours. We then use the corner detector of He and Yung [10]. It has shown excellent performance in applications involving real-world scenes compared to other popular feature detectors.…”

“…The detection of near-duplicate logos and unauthorized uses [8], [9]. Special applications of social utility have also been reported such as the recognition of groceries in stores for assisting the blind [10]. Different logos may have similar layout with slightly different spatial disposition of the graphic elements, localized differences in the orientation, size and shape, or in the case of malicious tampering -differ by the presence/absence of one or few traits [see Fig.…”