Interpretable aesthetic features for affective image classification

Images can not only display contents themselves, but also convey emotions, e.g., excitement, sadness. Affective image classification is useful and hot in many fields such as comput-er vision and multimedia. Current researches usually consid-er the relationship model between images and emotions as a black box. They extract the traditional discursive visual fea-tures such as SIFT and wavelet textures, and use them di-rectly upon various classification algorithms. However, these visual features are not interpretable, and people cannot know why such a set of features induce a particular emotion. And due to the highly subjective nature of images, the classifica-tion accuracies on these visual features are not satisfactory for a long time. We propose the interpretable aesthetic fea-tures to describe images inspired by art theories, which are intuitive, discriminative and easily understandable. Affective image classification based on these features can achieve high-er accuracy, compared with the state-of-the-art. Specifically, the features can also intuitively explain why an image tends to convey a certain emotion. We also develop an emotion guided image gallery to demonstrate the proposed feature collection. Index Terms — image features, affective classification, interpretability, art theory 1

Example-based image colorization using locality consistent sparse representation

—Image colorization aims to produce a natural looking color image from a given grayscale image, which remains a challenging problem. In this paper, we propose a novel examplebased image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target grayscale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target grayscale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms state-ofthe-art methods, both visually and quantitatively using a user stud

Parametric meta-filter modeling from a single example pair

We present a method for learning a meta- �lter from an example pair comprising an original image A and its �ltered version A0 using an unknown image �lter. A meta-�lter is a parametric model, consisting of a spatially varying linear combination of simple basis �lters. We introduce a technique for learning the parameters of the meta-�lter f such that it approximates the e�ects of the unknown �lter, i.e., f(A) approximates A0. The meta-�lter can be transferred to novel input images, and its parametric representation enables intuitive tuning of its parameters to achieve controlled variations. We show that our technique successfully learns and models meta-�lters that approximate a large variety of common image �lters with high accuracy both visually and quantitatively

Response of biological signals during acoustic and optical stimulation

Cílem této práce je záznam a vyhodnocení biologických signálů spjatých s činností autonomního nervového systému v reakci na podněty optického a akustického charakteru. Podněty vyvolávající změnu signálů jsou prezentovány ve formě obrázků. Týkají se změn barev, základních symbolů a kolorifikace neutrálních fotografií pomocí odlišných barevných vzorů. Akustická stimulace je zastoupena úlekovými podněty ve formě krátkých impulsů bílého šumu. Změny v signálech jsou statisticky či subjektivně vyhodnoceny jako změny v pocitech testované osoby. Pro získání komplexní informace vyplňují testované osoby dotazník, týkající se jejich pocitů z prezentovaných obrázků. Výsledky dotazníku jsou srovnány s odezvou signálů.The goal of this thesis is recording and evaluating of biological signals connected with autonomic nervous system. Changes in signals are caused by optical and acoustical stimuli. These stimuli are pictures of different meaning. Colors, basic symbols and neutral pictures with variance in colorization are included. Acoustic stimulation is provided by short startle stimulus of white noise sound impulse. Changes in signals are statistically and subjectively evaluated as changes in emotional state of tested person. Tested people fill in a questionnaire about their feelings. Results of signal processing and questionnaire are compared at the end.

A review of image and video colorization: From analogies to deep learning

Image colorization is a classic and important topic in computer graphics, where the aim is to add color to a monochromatic input image to produce a colorful result. In this survey, we present the history of colorization research in chronological order and summarize popular algorithms in this field. Early works on colorization mostly focused on developing techniques to improve the colorization quality. In the last few years, researchers have considered more possibilities such as combining colorization with NLP (natural language processing) and focused more on industrial applications. To better control the color, various types of color control are designed, such as providing reference images or color-scribbles. We have created a taxonomy of the colorization methods according to the input type, divided into grayscale, sketch-based and hybrid. The pros and cons are discussed for each algorithm, and they are compared according to their main characteristics. Finally, we discuss how deep learning, and in particular Generative Adversarial Networks (GANs), has changed this field