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Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints.We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photos. Our tour recommendation problem is modelled using a formulation of the Orienteering problem, and considers user trip constraints such as time limits and the need to start and end at specic POIs. In our work, we also reect levels of user interest based on visit durations, and demonstrate how POI visit duration can be personalized using this time-based user interest. Furthermore, we demonstrate how PersTour can be further enhanced by: (i) a weighted updating of user interests based on the recency of their POI visits; and (ii) an automatic weighting between POI popularity and user interests based on the tourist's activity level. Using a Flickr dataset of ten cities, our experiments show the eectiveness of PersTour against various collaborative ltering and greedy-based baselines, in terms of tour popularity, interest, recall, precision and F1-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

A visual model that gives a distortion measure for blocking artifacts in images is presented. Given the original and reproduced image as inputs, the model output is a numerical value that quantifies the visibility of blocking error in the reproduced image. The model is derived based on the human visual sensitivity to horizontal and vertical edge artifacts that result from blocking. Psychovisual experiments have been carried out to measure the visual sensitivity to these artifacts. In the experiments, typical edge artifacts are shown to subjects and the sensitivity to them is measured with the variation of background luminance, background activity, edge length, and edge amplitude. Synthetic test patterns are used as background images in the experiments. The sensitivity measures thus obtained are used to estimate the model parameters. The final model is tested on real images, and the results show that the error visibility predicted by the model correlates well with the subjective ranking.

Events detected from social media streams often include early signs of accidents, crimes or disasters. Therefore, they can be used by related parties for timely and efficient response. Although significant progress has been made on event detection from tweet streams, most existing methods have not considered the posted images in tweets, which provide richer information than the text, and potentially can be a reliable indicator of whether an event occurs or not. In this paper, we design an event detection algorithm that combines textual, statistical and image information, following an unsupervised machine learning approach. Specifically, the algorithm starts with semantic and statistical analyses to obtain a list of tweet clusters, each of which corresponds to an event candidate, and then performs image analysis to separate events from non-events-a convolutional autoencoder is trained for each cluster as an anomaly detector, where a part of the images are used as the training data and the remaining images are used as the test instances. Our experiments on multiple datasets verify that when an event occurs, the mean reconstruction errors of the training and test images are much closer, compared with the case where the candidate is a non-event cluster. Based on this finding, the algorithm rejects a candidate if the difference is larger than a threshold. Experimental results over millions of tweets demonstrate that this image analysis enhanced approach can significantly increase the precision with minimum impact on the recall.

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