It is absolutely clear that the world is full of structure. In photographs and video, objects, scenes, and events repeat over and over. Objects and scenes, in turn, have structures that characterize them objects have parts and parts have sub-parts. Many current automatic approaches to index visual information rely on specific algorithms constructed by experts. The goal of such algorithms (Visual Detectors) is to automatically label, or index visual content. While these algorithms can often be robust, it is clear that it is not possible to construct a large number of detectors by hand.
Therefore, for future applications it is desirable to build systems that allow the construction of programs that learn, from user input, how to automatically label data without the need of experts. Not only should such systems be scalable but they should also take into consideration users’ interests and subjectivity. This requires recognizing the structure inherent in visual information and exploiting such structure in computational frameworks. Without a doubt machine learning will form the basis of successful, scalable applications in the future. Those applications will change, and the way such algorithms learn will change. Perhaps learning will take place without explicit input from users.
Therefore, for future applications it is desirable to build systems that allow the construction of programs that learn, from user input, how to automatically label data without the need of experts. Not only should such systems be scalable but they should also take into consideration users’ interests and subjectivity. This requires recognizing the structure inherent in visual information and exploiting such structure in computational frameworks. Without a doubt machine learning will form the basis of successful, scalable applications in the future. Those applications will change, and the way such algorithms learn will change. Perhaps learning will take place without explicit input from users.
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