Ranking highlight level of movie clips : a template based adaptive kernel SVM method

This paper looks into a new direction in movie clips analysis – model based ranking of highlight level. A movie clip, containing a short story, is composed of several continuous shots, which is much simpler than the whole movie. As a result, clip based analysis provides a feasible way for movie analysis and interpretation. In this paper, clip-based ranking of highlight level is proposed, where the challenging problem in detecting and recognizing events within clips is not required. Due to the lack of publicly available datasets, we firstly construct a database of movie clips, where each clip is associated with manually derived highlight level as ground truth. From each clip a number of effective visual cues are then extracted. To bridge the gap between low-level features and highlight level semantics, a holistic method of highlight ranking model is introduced. According to the distance between testing clips and selected templates, appropriate kernel function of support vector machine (SVM) is adaptively selected. Promising results are reported in automatic ranking of movie highlight levels

Mining Emotional Features of Movies

ABSTRACT In this paper, we present the algorithm designed for mining emotional features of movies. The algorithm dubbed Arousal-Valence Discriminant Preserving Embedding (AV-DPE) is proposed to extract the intrinsic features embedded in movies that are essentially differentiating in both arousal and valence directions. After dimensionality reduction, we use the neural network and support vector regressor to make the final prediction. Experimental results show that the extracted features can capture most of the discriminant information in movie emotions

The MediaEval 2016 Emotional Impact of Movies Task

ABSTRACT This paper provides a description of the MediaEval 2016 "Emotional Impact of Movies" task. It continues builds on previous years' editions of the Affect in Multimedia Task: Violent Scenes Detection. However, in this year's task, participants are expected to create systems that automatically predict the emotional impact that video content will have on viewers, in terms of valence and arousal scores. Here we provide insights on the use case, task challenges, dataset and ground truth, task run requirements and evaluation metrics

Web Image re-ranking using Attribute Assisted Hypergraph

ABSTRAC

A Fast Clustering Algorithm based on pruning unnecessary distance computations in DBSCAN for High-Dimensional Data

Clustering is an important technique to deal with large scale data which are explosively created in internet. Most data are high-dimensional with a lot of noise, which brings great challenges to retrieval, classification and understanding. No current existing approach is “optimal” for large scale data. For example, DBSCAN requires O(n2) time, Fast-DBSCAN only works well in 2 dimensions, and ρ-Approximate DBSCAN runs in O(n) expected time which needs dimension D to be a relative small constant for the linear running time to hold. However, we prove theoretically and experimentally that ρ-Approximate DBSCAN degenerates to an O(n2) algorithm in very high dimension such that 2D >  > n. In this paper, we propose a novel local neighborhood searching technique, and apply it to improve DBSCAN, named as NQ-DBSCAN, such that a large number of unnecessary distance computations can be effectively reduced. Theoretical analysis and experimental results show that NQ-DBSCAN averagely runs in O(n*log(n)) with the help of indexing technique, and the best case is O(n) if proper parameters are used, which makes it suitable for many realtime data

The Emotional Impact of Audio - Visual Stimuli

Induced affect is the emotional effect of an object on an individual. It can be quantified through two metrics: valence and arousal. Valance quantifies how positive or negative something is, while arousal quantifies the intensity from calm to exciting. These metrics enable researchers to study how people opine on various topics. Affective content analysis of visual media is a challenging problem due to differences in perceived reactions. Industry standard machine learning classifiers such as Support Vector Machines can be used to help determine user affect. The best affect-annotated video datasets are often analyzed by feeding large amounts of visual and audio features through machine-learning algorithms. The goal is to maximize accuracy, with the hope that each feature will bring useful information to the table. We depart from this approach to quantify how different modalities such as visual, audio, and text description information can aid in the understanding affect. To that end, we train independent models for visual, audio and text description. Each are convolutional neural networks paired with support vector machines to classify valence and arousal. We also train various ensemble models that combine multi-modal information with the hope that the information from independent modalities benefits each other. We find that our visual network alone achieves state-of-the-art valence classification accuracy and that our audio network, when paired with our visual, achieves competitive results on arousal classification. Each network is much stronger on one metric than the other. This may lead to more sophisticated multimodal approaches to accurately identifying affect in video data. This work also contributes to induced emotion classification by augmenting existing sizable media datasets and providing a robust framework for classifying the same

Aesthetic Sensitivity

[eng] Aesthetic sensitivity is a central idea in the field of empirical aesthetics. The present research contributes a historical-critical review of its origin and development through the history of the discipline, a new theoretical approach aligned with current knowledge, novel methodological tools to investigate this and other relevant psychological constructs, and empirical evidence based on this conception that advances scientific understanding of sensory valuation.[spa] La sensibilidad estética es una idea central en el campo de la estética empírica. La presente investigación aporta una revisión histórico-crítica de su origen y desarrollo a través de la historia de la disciplina, un nuevo enfoque teórico de acuerdo con los conocimientos actuales, novedosas herramientas metodológicas para investigar éste y otros constructos psicológicos relevantes, y evidencia empírica basada en esta concepción que avanza la comprensión científica de la valoración sensorial.[cat] La sensibilitat estètica és una idea central en el camp de l'estètica empírica. La present investigació aporta una revisió històric-crítica del seu origen i desenvolupament a través de la història de la disciplina, un nou enfocament teòric alineat amb els coneixements actuals, noves eines metodològiques per investigar aquest i altres constructes psicològics rellevants, i evidència empírica basada en aquesta concepció que avança la comprensió científica de la valoració sensorial

Methods for Affective Content Analysis and Recognition in Film

The research presented in this thesis resulted from the growing attention on the effects of emotion on users, raising questions about their potential application to computational systems. This research investigates the best methods for determining affective scoring for video content, specifically films. This resulted in the affective video system (AVS) framework, AVS dataset and AVS systems being developed, leading to several contributions to knowledge about the best affective methods and systems. This work presents the necessary theory to understand the subject area. It builds as the thesis matures, laying a pathway in the form of a methodology framework for viewing affective problems and systems, moving into a subsequent study reviewing the well-recognised affective methods such as the International Affective Picture System (IAPS) and how its well-defined processes and procedures could be adapted for a more modern approach using video content. The research then studies the most critical perceivable features from video clips for users, which were analysed using the repertory grid approach. This led to the above contributions being combined to create the AVS system and database, which is a unique database comprising the affective scores for various film clips. This research concluded with the presentation of the best regression methods resulting from this research and its datasets and a summary of this performance, and discussions of the results in terms of other research in this area