Deep reinforcement learning-based image matching sequentially searches only the promising regions in the reference image that match the query, leading to a significantly small number of steps compared to traditional methods. Since existing methods do not have any function to judge whether the target region has been successfully identified or not, they continue to search until the preset maximum number of search steps is reached. In this paper, we propose a deep image matching network that can terminate the matching process by itself. Our network is designed to have a halting module that identifies whether the current reference region matches the query based on the image features and the search history. The entire network is effectively trained end-to-end in a framework of deep reinforcement learning that incorporates a new loss function to evaluate the accuracy of the termination decision. Experimental results demonstrate that our method can achieve highly competitive or better matching accuracy with fewer search steps than the existing methods.

Predicting which part of a scene elderly people would pay attention to could be useful in assisting their daily activities, such as driving, walking, and searching. Many computational models for predicting focus of attention (FoA) have been developed. However, most of them focus on mimicking adult FoA and do not work well for predicting elderly's, due to age-related changes in human vision. Is it possible to leverage the prediction results made by an FoA model of general adults to accurately predict elderly's FoA, rather than training a new network from scratch? In this paper, we consider a novel problem of translating adult's FoA to elderly's and propose an approach based on deep image-to-image translation. Our model is trained by minimizing both Kullback-Leibler divergence and adversarial loss to approximate the joint probability distribution of adult and elderly FoA. Experiments on two datasets demonstrate that our model gives remarkable prediction accuracy.

Finding a region of an image which matches to a query from a large number of candidates is a fundamental problem in image processing. The exhaustive nature of the sliding window approach has encouraged works that can reduce the run time by skipping unnecessary windows or pixels that do not play a substantial role in search results. However, such a pruning-based approach still needs to evaluate the non-ignorable number of candidates, which leads to a limited efficiency improvement. We propose an approach to learn efficient search paths from data. Our model is based on a CNN-LSTM architecture which is designed to sequentially determine a prospective location to be searched next based on the history of the locations attended. We propose a reinforcement learning algorithm to train the model in an end-to-end manner, which allows to jointly learn the search paths and deep image features for matching. These properties together significantly reduce the number of windows to be evaluated and makes it robust to background clutters. Our model gives remarkable matching accuracy with the reduced number of windows and run time on MNIST and FlickrLogos-32 datasets.

During the last few decades researchers in computer vision have proposed various saliency models for images with the common goal of classifying the image content by using the measure of importance. However, compared to still images, there is only a limited number of saliency detection algorithms proposed for video signals. However, predicting where a person looks in a video is relevant for applications such as advertisement design, video re-targeting and editing. In this work, we propose a novel method for video saliency detection that aims to detect the relative ranking of saliencies of signboards in street videos. For that reason, we collected eye-gaze data of participants viewing various street videos in free viewing and task viewing scenarios, where the task was to identify a place to have lunch at. Further, we quantitatively analyzed the collected eye-gaze data in order to generate the relative ranking of the signboards in the free viewing and the task viewing scenario. Based on the analysis' results, we propose a video saliency detection algorithm which can more accurately predict the relative saliencies of signboards in street videos. It can be seen that the prediction accuracy of our proposed model outperforms the existing video saliency detection algorithms.

Detecting salient locations in images and videos that attract human attention is a challenging problem in computer vision. Most of the models developed for this purpose are either based on the bottom-up features of a scene or top-down factors. Observer's age is one of the important top-down factor which influences viewing behavior, some recent studies proposed age-adapted models to predict saliencies for different age groups. However, these studies reported age-impact on eye-gaze distribution for images only and videos still remain unexplored. In this study, we analyze the differences in eye gaze landings between adults and elderly while viewing street videos and develop a novel application of detecting saliency of billboards in the paved area of the street for adult and elderly. Our proposed age-adapted saliency model outperforms the existing video saliency models towards predicting the billboard saliency.

Visual attention studies in computer vision research have focused on the development of computational attention systems that can detect salient regions in images for adults. Consequently, age differences in scene viewing behavior has rarely been considered. This study quantitatively analyzed the age-related differences in gaze landings during scene viewing for three age groups: children, adults, and elderly. An interesting observation from our analysis is that whereas child observers focus more on the scene foreground, i.e., locations that are near, elderly observers tend to explore the scene background, i.e., locations farther in the scene. Considering this result a framework is proposed in this paper to quantitatively measure the depth bias tendency across age groups. Further, the age impact on exploratory behavior, central bias tendency, and agreement between explored regions within and across the age groups are quantified via analysis. Experimental results show that children exhibit the lowest exploratory behavior level but the highest central bias tendency among the age groups. Further, agreement scores reveal that adults had least agreement with each other in explored regions. The data analysis results were consequently leveraged to develop a more accurate age-adapted saliency model that outperforms existing saliency models that do not consider age.

Nowadays multi-track conferences impose great difficulty to attendees in making a day plan of attending relevant sessions/papers, because a large number of sessions are scheduled on a single day and many of them are at the same time. To address this problem, we introduce MatPlanner, a mobile application, which provides an alternate way of scheduling a day in a conference by understanding the attendee's interest and preferences. MatPlanner learns event-topic interest matrix from few sampled events selected by each user and then recommend relevant events and display the final output. More importantly, MatPlanner's interface enables users to resolve conflicting events by showing them at the same panel which makes it easy to compare. Interview results suggested a high level of satisfaction among participants in scheduling a day.