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Large-scale instance-level image retrieval aims at retrieving specific instances of objects or scenes. Simultaneously retrieving multiple objects in a test image adds to the difficulty of the problem, especially if the objects are visually similar. This paper presents an efficient approach for per-exemplar multi-label image classification, which targets the recognition and localization of products in retail store images. We achieve runtime efficiency through the use of discriminative random forests, deformable dense pixel matching and genetic algorithm optimization. Cross-dataset recognition is performed, where our training images are taken in ideal conditions with only one single training image per product label, while the evaluation set is taken using a mobile phone in real-life scenarios in completely different conditions. In addition, we provide a large novel dataset and labeling tools for products image search, to motivate further research efforts on multi-label retail products image classification. The proposed approach achieves promising results in terms of both accuracy and runtime efficiency on 680 annotated images of our dataset, and 885 test images of GroZi-120 dataset. We make our dataset of 8350 different product images and the 680 test images from retail stores with complete annotations available to the wider community. Recently, image recognition in the retail products domain has become an interesting research topic due to the remarkable advancements in the capabilities D.

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Abstract. Radio Frequency Identification (RFID) has recently received a lot of attention as an augmentation technology in the ubiquitous computing domain. In this paper we present various sources of error in passive RFID systems, which can make the reliable operation of RFID augmented applications a challenge. To illustrate these sources of error, we equipped playing cards with RFID tags and measured the performance of the RFID system during the different stages of a typical card game. The paper also shows how appropriate system design can help to deal with the imperfections associated with RFID.

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The authors of this paper propose a means for providing low-cost, long-lifetime temperature-threshold sensing, displacement sensing, and fluid level sensing for RFID systems.

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