Abstract
Image annotation plays a vital role in dealing with effective organization and retrieval of a large number of digital images. Multi-instance multi-label (MIML) learning can deal with complicated objects by solving the ambiguity in both input and output space. Image bag generator is a key component of MIML algorithms. A bag generator takes an image as its input and generates a set of instances for that image. These instances are the various subparts of the original image and collectively describe the image in totality. This paper proposes two new bag generators which can generate an instance for every possible object present in the image. The proposed bag generators effectively utilize the correlations among pixels to generate instances. We demonstrate that the proposed bag generators outperform the state-of-the-art bag generator methods.
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Bhagat, P.K., Choudhary, P., Singh, K.M. (2020). Two Efficient Image Bag Generators for Multi-instance Multi-label Learning. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1147. Springer, Singapore. https://doi.org/10.1007/978-981-15-4015-8_36
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