CROPPING TECHNIQUES AND RESULT COMPARISON: RELATED WORK(2)

RELATED WORK(2)

The problem is of selecting iconic images to summarize general visual categories. The author defined iconic images as high-quality representatives of a large group of images consistent both in appearance and semantics. The approach to find such groups was to perform joint clustering in the space of global image descriptors and latent topic vectors of tags associated with the images. The author has also used a ranking mechanism ranking learned from a large collection of labeled images. The key difference, it’s assumed that there is one iconic view of the scene rather than a diverse set of representative views as we show in this work.

In the absence of location metadata, temporal metadata was also considered in the past for the purpose of photo collection summarization. Graham et al. describe an algorithm to heuristically select representative photos for a given time period in a personal collection, utilizing patterns in human photo-taking habits. Additional time-based work aims to detect events in personal collections, which could be the basis for collection summarization. However, again, all these projects considered singlephotographer collections only. Several projects use geographic data to organize photo collections in novel ways, for example, by detecting significant events and locations in a photo collection. Such structures could indeed be the basis for collection summarization. However, we don’t have geographic data of images.

In general our work is different in terms of windows generating technique in initial stage and ranking mechanism.

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