2 resultados para Audio-visual content classification

em DRUM (Digital Repository at the University of Maryland)


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Though the trend rarely receives attention, since the 1970s many American filmmakers have been taking sound and music tropes from children’s films, television shows, and other forms of media and incorporating those sounds into films intended for adult audiences. Initially, these references might seem like regressive attempts at targeting some nostalgic desire to relive childhood. However, this dissertation asserts that these children’s sounds are instead designed to reconnect audience members with the multi-faceted fantasies and coping mechanisms that once, through children’s media, helped these audience members manage life’s anxieties. Because sound is the sense that Western audiences most associate with emotion and memory, it offers audiences immediate connection with these barely conscious longings. The first chapter turns to children’s media itself and analyzes Disney’s 1950s forays into television. The chapter argues that by selectively repurposing the gentlest sonic devices from the studio’s films, television shows like Disneyland created the studio’s signature sentimental “Disney sound.” As a result, a generation of baby boomers like Steven Spielberg comes of age and longs to recreate that comforting sound world. The second chapter thus focuses on Spielberg, who incorporates Disney music in films like Close Encounters of the Third Kind (1977). Rather than recreate Disney’s sound world, Spielberg uses this music as a springboard into a new realm I refer to as “sublime refuge” - an acoustic haven that combines overpowering sublimity and soothing comfort into one fantastical experience. The second half of the dissertation pivots into more experimental children’s cartoons like Gerald McBoing-Boing (1951) - cartoons that embrace audio-visual dissonance in ways that soothe even as they create tension through a phenomenon I call “comfortable discord.” In the final chapter, director Wes Anderson reveals that these sonic tensions have just as much appeal to adults. In films like The Royal Tenenbaums (2001), Anderson demonstrates that comfortable discord can simultaneously provide a balm for anxiety and create an open-ended space that makes empathetic connections between characters possible. The dissertation closes with a call to rethink nostalgia, not as a romanticization of the past, but rather as a reconnection with forgotten affective channels.

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Increasing the size of training data in many computer vision tasks has shown to be very effective. Using large scale image datasets (e.g. ImageNet) with simple learning techniques (e.g. linear classifiers) one can achieve state-of-the-art performance in object recognition compared to sophisticated learning techniques on smaller image sets. Semantic search on visual data has become very popular. There are billions of images on the internet and the number is increasing every day. Dealing with large scale image sets is intense per se. They take a significant amount of memory that makes it impossible to process the images with complex algorithms on single CPU machines. Finding an efficient image representation can be a key to attack this problem. A representation being efficient is not enough for image understanding. It should be comprehensive and rich in carrying semantic information. In this proposal we develop an approach to computing binary codes that provide a rich and efficient image representation. We demonstrate several tasks in which binary features can be very effective. We show how binary features can speed up large scale image classification. We present learning techniques to learn the binary features from supervised image set (With different types of semantic supervision; class labels, textual descriptions). We propose several problems that are very important in finding and using efficient image representation.