Abstract :
In the last couple of decades, radio-frequency identification (RFID) technology has been widely used in logistics, manufacturing, defense, environment, health care, agriculture, retail, aviation, and information technology. CBIR systems go through sets of stages starting from acquiring the new images, representing these images by extracting the image features, describing the key features and eventually computing the similarity distances to get the most relevant results responding to the query image. In this paper, an integrated CBIR Hadoop-MapReduce based framework which is split into both offline and online phases is introduced. Visual statements are built using the extracted interest points SIFTs. Later on, these visual statements are used to estimate the similarity distances which in turn are used to create the image dataset clusters. A huge vocabulary of SIFTs describing the interest points of the image is constructed. Corresponding statements which reflect the visual content for these features are created by applying the HAC technique.
