Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models

Deep convolutional neural networks have recently achieved great success on image aesthetics assessment task. In this paper, we propose an efficient method which takes the global, local and scene-aware information of images into consideration and exploits the composite features extracted from corresponding pretrained deep learning models to classify the derived features with support vector machine. Contrary to popular methods that require fine-tuning or training a new model from scratch, our training-free method directly takes the deep features generated by off-the-shelf models for image classification and scene recognition. Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information. It turns out that deep residual network could produce more aesthetics-aware image representation and composite features lead to the improvement of overall performance. Experiments on common large-scale aesthetics assessment benchmarks demonstrate that our method outperforms the state-of-the-art results in photo aesthetics assessment.Comment: Accepted by ICIP 201

Textiles as Material Gestalt: Cloth as a Catalyst in the Co-designing Process

Textiles is the common language within Emotional Fit, a collaborative research project investigating a person-centred, sustainable approach to fashion for an ageing female demographic (55+). Through the co-designing of a collection of research tools, textiles have acted as a material gestalt for exploring our research participants' identities by tracing their embodied knowledge of fashionable dress. The methodology merges Interpretative Phenomenological Analysis, co-design and a simultaneous approach to textile and garment design. Based on an enhanced understanding of our participants textile preferences, particular fabric qualities have catalysed silhouettes, through live draping and geometric pattern cutting to accommodate multiple body shapes and customisation. Printedtextiles have also been digitally crafted in response to the contours of the garment and body and personal narratives of wear. Sensorial and tactile interactions have informed the engineering and scaling of patterns within zero-waste volumes. The article considers the functional and aesthetic role of textiles

Some like it hot - visual guidance for preference prediction

For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already pointed out that age, gender, and average attractiveness can be estimated with reasonable precision. We improve the state-of-the-art, but also predict - based on someone's known preferences - how much that particular person is attracted to a novel face. Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer inter-person preferences as well as a novel regression technique for handling visual queries without rating history. We validate the method using a very large dataset from a dating site as well as images from celebrities. Our experiments yield convincing results, i.e. we predict 76% of the ratings correctly solely based on an image, and reveal some sociologically relevant conclusions. We also validate our collaborative filtering solution on the standard MovieLens rating dataset, augmented with movie posters, to predict an individual's movie rating. We demonstrate our algorithms on howhot.io which went viral around the Internet with more than 50 million pictures evaluated in the first month.Comment: accepted for publication at CVPR 201

Poverty and the Picture Gallery : the Whitechapel Exhibitions and the Social Project of Ruskinian Aesthetics

Peer reviewedSubmitted Versio