Category Archives: Photography

CAPTAIN: Comprehensive Composition Assistance for Photo Taking

Abstract: Many people are interested in taking astonishing photos and sharing with others. Emerging high-tech hardware and software facilitate ubiquitousness and functionality of digital photography. Because composition matters in photography, researchers have leveraged some common composition techniques, such as the rule of thirds, the triangle technique, and the perspective-related techniques, to assess the aesthetic quality of photos computationally. However, composition techniques developed by professionals are far more diverse than well-documented techniques can cover. We leverage the vast under-explored innovations in photography for computational composition assistance, and there is a lack of a holistic framework to capture important aspects of a given scene and help individuals by constructive clues to take a better shot in their adventure. We propose a comprehensive framework, named CAPTAIN (Composition Assistance for Photo Taking), containing integrated deep-learned semantic detectors, sub-genre categorization, artistic pose clustering, personalized aesthetics-based image retrieval, and style set matching. The framework is backed by a large dataset crawled from a photo-sharing Website with mostly photography enthusiasts and professionals.
The work proposes a sequence of steps that have not been explored in the past by researchers.
The work addresses personal preferences for composition through presenting a ranked-list of photographs to the user based on user-specified weights in the similarity measure. We believe our design leveraging user-defined preferences. Our framework extracts ingredients of a given snapshot of a scene (i.e. the scene that the user is interested in taking a picture of) as a set of composition-related features ranging from low-level features such as color, pattern, and texture to high-level features such as pose, category, rating, gender, and object. Our composition model, indexed offline, is used to provide visual exemplars as recommendations for the scene, which is a novel model for aesthetics-related image retrieval. We believe our design leveraging user-defined preferences The matching algorithm recognizes the best shot among a sequence of shots with respect to the user’s preferred style set. We have conducted a number of experiments on the newly proposed components and reported findings. A user study demonstrates that the work is useful to those taking photos.

Keywords: Computational Composition, Image Aesthetics, Photography, Deep Learning, Image Retrieval

captain_springer_ijcv

Deep-learned Models and Photography Idea Retrieval

Intelligent Portrait Composition Assistance (IPCA)
Farshid Farhat, Mohammad Kamani, Sahil Mishra, James Wang
ACM Multimedia 2017, Mountain View, CA, USA
(Acceptance rate = 64/495 = 12.93%)

ABSTRACT: Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures some aesthetic techniques such as the rule of thirds, triangle, and perspective, and retrieves useful feedbacks based on one technique. However, they are restricted to a particular technique and the retrieved results have room to improve as they can be limited to the quality of the query. There is a lack of a holistic framework to capture important aspects of a given scene and give a novice photographer informative feedback to take a better shot in his/her photography adventure. This work proposes an intelligent framework of portrait composition using our deep-learned models and image retrieval methods. A highly-rated web-crawled portrait dataset is exploited for retrieval purposes. Our framework detects and extracts ingredients of a given scene representing as a correlated hierarchical model. It then matches extracted semantics with the dataset of aesthetically composed photos to investigate a ranked list of photography ideas, and gradually optimizes the human pose and other artistic aspects of the composed scene supposed to be captured. The conducted user study demonstrates that our approach is more helpful than the other constructed feedback retrieval systems.

Please cite our paper if you are using our professional portrait dataset.

@inproceedings{Farhat:2017:IPC:3126686.3126710,
author = {Farhat, Farshid and Kamani, Mohammad Mahdi and Mishra, Sahil and Wang, James Z.},
title = {Intelligent Portrait Composition Assistance: Integrating Deep-learned Models and Photography Idea Retrieval},
booktitle = {Proceedings of the on Thematic Workshops of ACM Multimedia 2017},
series = {Thematic Workshops ’17},
year = {2017},
isbn = {978-1-4503-5416-5},
location = {Mountain View, California, USA},
pages = {17–25},
numpages = {9},
url = {http://doi.acm.org/10.1145/3126686.3126710},
doi = {10.1145/3126686.3126710},
acmid = {3126710},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {deep learning, image aesthetics, image retrieval., photographic composition, portrait photography, smart camera},
}

 

Discovering Triangles in Portraits and Landscapes

ABSTRACT

Incorporating the concept of triangles in photos is an effective composition method used by professional photographers for making pictures more interesting or dynamic. Information on the locations of the embedded triangles is valuable for comparing the composition of portrait photos, which can be further leveraged by a retrieval system or used by photographers. This paper presents a system to automatically detect embedded triangles in portrait photos. The problem is challenging because the triangles used in portraits are often not clearly defined

by straight lines. The system first extracts a set of filtered line segments as candidate triangle sides, and then utilizes a modified RANSAC algorithm to fit triangles onto the set of line segments. We propose two metrics, Continuity Ratio and Total Ratio, to evaluate the fitted triangles; those with high fitting scores are taken as detected triangles. Experimental results have demonstrated high accuracy in locating preeminent triangles in portraits without dependence on the camera or lens parameters. To demonstrate the benefits of our method to digital photography, we have developed two novel applications that aim to help users composing high-quality photos. In the first application, we develop a human position and pose recommendation system by retrieving and presenting compositionally similar photos taken by competent photographers. The second application is a novel sketch-based triangle retrieval system which searches for photos containing specific triangular configuration. User studies have been conducted to validate the effectiveness of these approaches.

Detecting Dominant Vanishing Points in Natural Scenes

Abstract:

Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the inadequate number of strong edges converging to the dominant vanishing point is inadequate. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.

Full Text > Vanishing_Point_Detection_Landscape

Dataset (Landscape photos from AVA and Flickr)

Natural scenes with vanishing points