We investigate human interest in photos. Based on our own and others' psychological experiments, we identify various cues for "interestingness", namely aesthetics, unusualness and general preferences. For the ranking of retrieved images, interestingness is more appropriate than cues proposed earlier. Interestingness is, for example, correlated with what people believe they will remember. This is opposed to actual memorability, which is uncorrelated to both of them. We introduce a set of features computationally capturing the three main aspects of visual interestingness that we propose and build an interestingness predictor from them. Its performance is shown on three datasets with varying context, reflecting diverse levels of prior knowledge of the viewers.

Unexpected stimuli are a challenge to any machine learning algorithm. Here, we identify distinct types of unexpected events when general-level and specific-level classifiers give conflicting predictions. We define a formal framework for the representation and processing of incongruent events: Starting from the notion of label hierarchy, we show how partial order on labels can be deduced from such hierarchies. For each event, we compute its probability in different ways, based on adjacent levels in the label hierarchy. An incongruent event is an event where the probability computed based on some more specific level is much smaller than the probability computed based on some more general level, leading to conflicting predictions. Algorithms are derived to detect incongruent events from different types of hierarchies, different applications, and a variety of data types. We present promising results for the detection of novel visual and audio objects, and new patterns of motion in video. We also discuss the detection of Out-Of- Vocabulary words in speech recognition, and the detection of incongruent events in a multimodal audiovisual scenario.

No abstract

Interestingness is said to be the power of attracting or holding one's attention (because something is unusual or exciting, etc.). We, as humans, have the great capacity to direct our visual attention and judge the interestingness of a scene. Consider for example the image sequence in the figure on the right. The spider in front of the camera or the snow on the lens are examples of events that deviate from the context since they violate the expectations, and therefore are considered interesting. On the other hand, weather changes or a camera shift, do not raise human attention considerably, even though large regions of the image are influenced. In this work we firstly investigate what humans consider as "interesting" in image sequences. Secondly we propose a computer vision algorithm to automatically spot these interesting events. To this end, we integrate multiple cues inspired by cognitive concepts and discuss why and to what extent the automatic discovery of visual interestingness is possible. ABSTRACTInterestingness is said to be the power of attracting or holding one's attention (because something is unusual or exciting, etc.). 1 We, as humans, have the great capacity to direct our visual attention and judge the interestingness of a scene. Consider for example the image sequence in the figure on the right. The spider in front of the camera or the snow on the lens are examples of events that deviate from the context since they violate the expectations, and therefore are considered interesting. On the other hand, weather changes or a camera shift, do not raise human attention considerably, even though large regions of the image are influenced. In this work we firstly investigate what humans consider as "interesting" in image sequences. Secondly we propose a computer vision algorithm to automatically spot these interesting events. To this end, we integrate multiple cues inspired by cognitive concepts and discuss why and to what extent the automatic discovery of visual interestingness is possible. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. Categories and Subject Descriptors

Figure 1: In videos, each frame strongly correlates with its neighbors. Our approach exploits this fact and enables the segmentation of the video and the interpretation of unseen sequences. AbstractTemporal consistency is a strong cue in continuous data streams and especially in videos. We exploit this concept and encode temporal relations between consecutive frames using discriminative slow feature analysis. Activities are automatically segmented and represented in a hierarchical coarse to fine structure. Simultaneously, they are modeled in a generative manner, in order to analyze unseen data. This analysis supports the detection of previously learned activities and of abnormal, novel patterns. Our technique is purely data-driven and feature-independent. Experiments validate the approach in several contexts, such as traffic flow analysis and the monitoring of human behavior. The results are competitive with the state-of-the-art in all cases.

No abstract

One of the great open challenges in visual recognitionis the ability to cope with unexpected stimuli. In this work, we present a technique to interpret detected anomalies and update the existing knowledge of normal situations. The addressed context is the analysis of human behavior in indoor surveillance scenarios, where new activities might need to be learned, once the system is already in operation. Our approach is based on human tracking with multiple activity trackers. The main contribution is to integrate a learning stage, where labeled and unlabeled information is collected and analyzed. To this end we develop a new multiclass version of transfer learning which requires minimal human interaction but still provides semantic labels of the new classes. The activity model is then updated with the new activities. Experiments show promising results.

No abstract