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Coral species, with complex morphology and ambiguous boundaries, pose a great challenge for automated classification. CNN activations, which are extracted from fully connected layers of deep networks (FC features), have been successfully used as powerful universal representations in many visual tasks. In this paper, we investigate the transferability and combined performance of FC features and CONV features (extracted
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![LDADeep+ utilizes the high-level meaning of deep learning representation, and combines it with topic model to learn good aspects LDADeep+ utilizes the high-level meaning of deep learning representation, and combines it with topic model to learn good aspects](https://sigport.org/sites/default/files/styles/list/public/Media%20Root/Screen%20Shot%202016-03-19%20at%206.23.21%20PM_2.png?itok=-rODtUcD)
Nowadays, with the success and fast growth of social media communities and mobile devices, people are encouraged to share their multimedia data online. Analyzing and summarizing data into useful information thus becomes increasingly important. For on- line photo sharing services like Flickr, when users are uploading a batch of daily photos at a time, the tags users provided tend to be rather vague, containing only a small amount of information.
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