Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities of photographs is a highly subjective task. Hence, there is no unanimously agreed standard for measuring aesthetic value. In spite of the lack of firm rules, certain features in photographic images are believed, by many, to please humans more than certain others. In this paper, we treat the challenge of automatically inferring aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated online photo sharing Website as data source. We extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images. Automated classifiers are built using support vector machines and classification trees. Linear regression on polynomial terms of the features is also applied to infer numerical aesthetics ratings. The work attempts to explore the relationship between emotions which pictures arouse in people, and their low-level content. Potential applications include content-based image retrieval and digital photography.

I n this tutorial, we define and discuss key aspects of the problem of computational inference of aesthetics and emotion from images. We begin with a background discussion on philosophy, photography, paintings, visual arts, and psychology. This is followed by introduction of a set of key computational problems that the research community has been striving to solve and the computational framework required for solving them. We also describe data sets available for performing assessment and outline several real-world applications where research in this domain can be employed. A significant number of papers that have attempted to solve problems in aesthetics and emotion inference are surveyed in this tutorial. We also discuss future directions that researchers can pursue and make a strong case for seriously attempting to solve problems in this research domain.

This article provides a color-based image retrieval technique for RGB image databases. Our proposed CBIR system uses the query by example approach and a relevance feedback mechanism. Feature extraction process is performed by computing a global color histogram for each image. Feature vectors are compared using the histogram intersection difference metric, and a relevance feedback mechanism is used in the retrieval process.

Initial studies have shown that automatic inference of high-level image quality or aesthetics is very challenging. The ability to do so, however, can prove beneficial in many applications. In this paper, we define the aesthetics gap and discuss key aspects of the problem of aesthetics and emotion inference in natural images. We introduce precise, relevant questions to be answered, the effect that the target audience has on the problem specification, broad technical solution approaches, and assessment criteria. We then report on our effort to build real-world datasets that provide viable approaches to test and compare algorithms for these problems, presenting statistical analysis of and insights into them.

We report on a new algorithm for combining the information from several mass spectra of the same peptide. The algorithm automatically learns peptide fragmentation patterns, so that it can handle spectra from any instrument and fragmentation technique. We demonstrate the utility of the algorithm, and the power of multiple spectra, by showing that combining pairs of spectra (one CID and one ETD) greatly improves de novo sequencing success rates.