Abstract-This paper looks into a new direction in video content analysis -the representation and modeling of affective video content. The affective content of a given video clip can be defined as the intensity and type of feeling or emotion (both are referred to as affect) that are expected to arise in the user while watching that clip. The availability of methodologies for automatically extracting this type of video content will extend the current scope of possibilities for video indexing and retrieval. For instance, we will be able to search for the funniest or the most thrilling parts of a movie, or the most exciting events of a sport program. Furthermore, as the user may want to select a movie not only based on its genre, cast, director and story content, but also on its prevailing mood, the affective content analysis is also likely to contribute to enhancing the quality of personalizing the video delivery to the user. We propose in this paper a computational framework for affective video content representation and modeling. This framework is based on the dimensional approach to affect that is known from the field of psychophysiology. According to this approach, the affective video content can be represented as a set of points in the two-dimensional (2-D) emotion space that is characterized by the dimensions of arousal (intensity of affect) and valence (type of affect). We map the affective video content onto the 2-D emotion space by using the models that link the arousal and valence dimensions to low-level features extracted from video data. This results in the arousal and valence time curves that, either considered separately or combined into the so-called affect curve, are introduced as reliable representations of expected transitions from one feeling to another along a video, as perceived by a viewer.Index Terms-Affective video content analysis, video abstraction, video content modeling, video content representation, video highlights extraction.

Cross-modal retrieval aims to enable flexible retrieval experience across different modalities (e.g., texts vs. images). The core of crossmodal retrieval research is to learn a common subspace where the items of different modalities can be directly compared to each other. In this paper, we present a novel Adversarial Cross-Modal Retrieval (ACMR) method, which seeks an effective common subspace based on adversarial learning. Adversarial learning is implemented as an interplay between two processes. The first process, a feature projector, tries to generate a modality-invariant representation in the common subspace and to confuse the other process, modality classifier, which tries to discriminate between different modalities based on the generated representation. We further impose triplet constraints on the feature projector in order to minimize the gap among the representations of all items from different modalities with same semantic labels, while maximizing the distances among semantically different images and texts. Through the joint exploitation of the above, the underlying cross-modal semantic structure of multimedia data is better preserved when this data is projected into the common subspace. Comprehensive experimental results on four widely used benchmark datasets show that the proposed ACMR method is superior in learning effective subspace representation and that it significantly outperforms the state-of-the-art cross-modal retrieval methods. CCS CONCEPTS• Information systems → Multimedia and multimodal retrieval;

Abstract-Partitioning a video sequence into shots is the first step toward video-content analysis and content-based video browsing and retrieval. A video shot is defined as a series of interrelated consecutive frames taken contiguously by a single camera and representing a continuous action in time and space. As such, shots are considered to be the primitives for higher level content analysis, indexing, and classification. The objective of this paper is twofold. First, we analyze the shot-boundary detection problem in detail and identify major issues that need to be considered in order to solve this problem successfully. Then, we present a conceptual solution to the shot-boundary detection problem in which all issues identified in the previous step are considered. This solution is provided in the form of a statistical detector that is based on minimization of the average detection-error probability. We model the required statistical functions using a robust metric for visual content discontinuities (based on motion compensation) and take into account all (a priori) knowledge that we found relevant to shot-boundary detection. This knowledge includes the shot-length distribution, visual discontinuity patterns at shot boundaries, and characteristic temporal changes of visual features around a boundary. Major advantages of the proposed detector are its robust and sequence-independent performance, while there is also the possibility to detect different types of shot boundaries simultaneously. We demonstrate the performance of our detector regarding two most widely used types of shot boundaries: hard cuts and dissolves.Index Terms-Shot-boundary detection, video analysis, video databases, video retrieval.

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