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[Music] in this video we will learn to extract the semantic features used as digital signature for retrieval of similar images we will start with somewhat less sophisticated approaches and trace the development of semantic features up to more recent results to search for a target image one first needs to compute its semantic representation from raw pixel values virtually every approach to computing features for image classification and recognition has been applied to image retrieval too starting from basic color histograms the research then went on to using gradient histograms such as hog or sift not surprisingly features extracted from deep convolutional neural networks have recently gained attention as an effective image representation one of the first content-based image retrieval systems was the was the cubic system developed at ibm in 1995. it worked in two modes image search using color histograms and image search using object masks specified by user the second mode was possible
