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A R T I C L E I N F O A B S T R A C T Article history: Received: 16 October, 2020 Accepted: 24 November, 2020 Online: 16 December, 2020 The huge number of images on the image sharing websites poses challenges for classification and retrieval of the images. On many image sharing websites, tags can be assigned by the users to an image that describes the contextual and visual description of an image. However, ambiguous or incorrect tags have appeared in frequent tags that affect the performance of an image retrieval system. Thus, assigning appropriate tags to the images plays a very important role in image retrieval and classification. In this paper, the ITR-WTF image tag recommendation method is proposed which explores tags from ranked nearest neighbors of each category. For a given input image, the method first determines the neighbors from training images of each category and ranks the neighbors according to the distance from the input image. In the second step, the weight is assigned to each tag based on the vote from each neighbor. Finally, the weighted tag frequency is determined to recommend appropriate tags to a given image. The experimentation is done on two datasets self-generated and NUS-WIDE. The results obtained using the proposed method ITR-WTF gives good results as compared with the existing methods of tag recommendation.

Image Tag Recommendation based on Ranked Categorical Nearest Neighbors and Weighted Tag Features