Results 1 - 10
of
131
Image retrieval: ideas, influences, and trends of the new age
- ACM COMPUTING SURVEYS
, 2008
"... We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger ass ..."
Abstract
-
Cited by 485 (13 self)
- Add to MetaCart
(Show Context)
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
Photo and video quality evaluation: Focusing on the subject
- In Proceedings of the European Conference on Computer Vision
"... Abstract. Traditionally, distinguishing between high quality professional pho-tos and low quality amateurish photos is a human task. To automatically assess the quality of a photo that is consistent with humans perception is a challenging topic in computer vision. Various differences exist between p ..."
Abstract
-
Cited by 66 (3 self)
- Add to MetaCart
(Show Context)
Abstract. Traditionally, distinguishing between high quality professional pho-tos and low quality amateurish photos is a human task. To automatically assess the quality of a photo that is consistent with humans perception is a challenging topic in computer vision. Various differences exist between photos taken by pro-fessionals and amateurs because of the use of photography techniques. Previous methods mainly use features extracted from the entire image. In this paper, based on professional photography techniques, we first extract the subject region from a photo, and then formulate a number of high-level semantic features based on this subject and background division. We test our features on a large and diverse photo database, and compare our method with the state of the art. Our method performs significantly better with a classification rate of 93 % versus 72 % by the best existing method. In addition, we conduct the first study on high-level video quality assessment. Our system achieves a precision of over 95 % in a reason-able recall rate for both photo and video assessments. We also show excellent application results in web image search re-ranking. 1
High level describable attributes for predicting aesthetics and interestingness
- In CVPR
, 2011
"... With the rise in popularity of digital cameras, the amount of visual data available on the web is growing exponentially. Some of these pictures are extremely beautiful and aesthetically pleasing, but the vast majority are uninteresting or of low quality. This paper demonstrates a simple, yet powerfu ..."
Abstract
-
Cited by 51 (1 self)
- Add to MetaCart
(Show Context)
With the rise in popularity of digital cameras, the amount of visual data available on the web is growing exponentially. Some of these pictures are extremely beautiful and aesthetically pleasing, but the vast majority are uninteresting or of low quality. This paper demonstrates a simple, yet powerful method to automatically select high aesthetic quality images from large image collections. Our aesthetic quality estimation method explicitly predicts some of the possible image cues that a human might use to evaluate an image and then uses them in a discriminative approach. These cues or high level describable image attributes fall into three broad types: 1) compositional attributes related to image layout or configuration, 2) content attributes related to the objects or scene types depicted, and 3) sky-illumination attributes related to the natural lighting conditions. We demonstrate that an aesthetics classifier trained on these describable attributes can provide a significant improvement over baseline methods for predicting human quality judgments. We also demonstrate our method for predicting the “interestingness ” of Flickr photos, and introduce a novel problem of estimating query specific “interestingness”. 1.
Automatic salient object segmentation based on context and shape prior
- In Proc. British Machine Vision Conference (BMVC
, 2011
"... We propose a novel automatic salient object segmentation algorithm which integrates both bottom-up salient stimuli and object-level shape prior, i.e., a salient object has a well-defined closed boundary. Our approach is formalized as an iterative energy minimization framework, leading to binary segm ..."
Abstract
-
Cited by 38 (5 self)
- Add to MetaCart
We propose a novel automatic salient object segmentation algorithm which integrates both bottom-up salient stimuli and object-level shape prior, i.e., a salient object has a well-defined closed boundary. Our approach is formalized as an iterative energy minimization framework, leading to binary segmentation of the salient object. Such energy minimization is initialized with a saliency map which is computed through context analysis based on multi-scale superpixels. Object-level shape prior is then extracted combining saliency with object boundary information. Both saliency map and shape prior update after each iteration. Experimental results on two public benchmark datasets show that our proposed approach outperforms state-of-the-art methods. 1
Image Partial Blur Detection and Classification
- Proc. IEEE Conf. Computer Vision and Pattern Recognition
, 2008
"... In this paper, we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring. We develop several blur fea ..."
Abstract
-
Cited by 36 (2 self)
- Add to MetaCart
(Show Context)
In this paper, we propose a partially-blurred-image classification and analysis framework for automatically detecting images containing blurred regions and recognizing the blur types for those regions without needing to perform blur kernel estimation and image deblurring. We develop several blur features modeled by image color, gradient, and spectrum information, and use feature parameter training to robustly classify blurred images. Our blur detection is based on image patches, making region-wise training and classification in one image efficient. Extensive experiments show that our method works satisfactorily on challenging image data, which establishes a technical foundation for solving several computer vision problems, such as motion analysis and image restoration, using the blur information. 1.
A Framework for Photo-Quality Assessment and Enhancement based on Visual Aesthetics
"... We present an interactive application that enables users to improve the visual aesthetics of their digital photographs using spatial recomposition. Unlike earlier work that focuses either on photo quality assessment or interactive tools for photo editing, we enable the user to make informed decision ..."
Abstract
-
Cited by 27 (0 self)
- Add to MetaCart
(Show Context)
We present an interactive application that enables users to improve the visual aesthetics of their digital photographs using spatial recomposition. Unlike earlier work that focuses either on photo quality assessment or interactive tools for photo editing, we enable the user to make informed decisions about improving the composition of a photograph and to implement them in a single framework. Specifically, the user interactively selects a foreground object and the system presents recommendations for where it can be moved in a manner that optimizes a learned aesthetic metric while obeying semantic constraints. For photographic compositions that lack a distinct foreground object, our tool provides the user with cropping or expanding recommendations that improve its aesthetic quality. We learn a support vector regression model for capturing image aesthetics from user data and seek to optimize this metric during recomposition. Rather than prescribing a fully-automated solution, we allow user-guided object segmentation and inpainting to ensure that the final photograph matches the user’s criteria. Our approach achieves 86 % accuracy in predicting the attractiveness of unrated images, when compared to their respective human rankings. Additionally, 73 % of the images recomposited using our tool are ranked more attractive than their original counterparts by human raters.
Large-scale visual sentiment ontology and detectors using adjective noun pairs
- In ACM MM
, 2013
"... We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key con ..."
Abstract
-
Cited by 23 (7 self)
- Add to MetaCart
(Show Context)
We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (AN-P). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.
Sensationbased photo cropping
- In Proc. ACM MM
"... This paper proposes a novel method for automatically crop-ping a photo using a quality classifier that assesses whether the cropped region is agreeable to users. We statistically build this quality classifier using large photo collections avail-able on websites where people manually insert quality s ..."
Abstract
-
Cited by 23 (1 self)
- Add to MetaCart
(Show Context)
This paper proposes a novel method for automatically crop-ping a photo using a quality classifier that assesses whether the cropped region is agreeable to users. We statistically build this quality classifier using large photo collections avail-able on websites where people manually insert quality scores to photos. We first trim the original image and then decide on the candidates for cropping. We find the cropped region with the highest quality score by applying the quality clas-sifier to the candidates. Current automatic photo cropping techniques search for attention grabbing regions that con-sist of salient pixels from the original photo. They are not always pleasant to users because they do not take into ac-count the quality of the cropped region. Our method with the quality classifier outperforms a state-of-the-art method that takes into consideration only the user’s attention for automatic photo cropping.
X.: Content-based photo quality assessment
- In: ICCV (2011
"... Automatically assessing photo quality from the perspec-tive of visual aesthetics is of great interest in high-level vi-sion research and has drawn much attention in recent years. In this paper, we propose content-based photo quality as-sessment using regional and global features. Under this framewor ..."
Abstract
-
Cited by 18 (1 self)
- Add to MetaCart
(Show Context)
Automatically assessing photo quality from the perspec-tive of visual aesthetics is of great interest in high-level vi-sion research and has drawn much attention in recent years. In this paper, we propose content-based photo quality as-sessment using regional and global features. Under this framework, subject areas, which draw the most attentions of human eyes, are first extracted. Then regional features extracted from subject areas and the background regions are combined with global features to assess the photo qual-ity. Since professional photographers may adopt different photographic techniques and may have different aesthetic criteria in mind when taking different types of photos (e.g. landscape versus portrait), we propose to segment region-s and extract visual features in different ways according to the categorization of photo content. Therefore we divide the photos into seven categories based on their content and de-velop a set of new subject area extraction methods and new visual features, which are specially designed for different categories. This argument is supported by extensive exper-imental comparisons of existing photo quality assessment approaches as well as our new regional and global features over different categories of photos. Our new features sig-nificantly outperform the state-of-the-art methods. Another contribution of this work is to construct a large and diver-sified benchmark database for the research of photo quality assessment. It includes 17, 613 photos with manually la-beled ground truth. 1.
Computing Iconic Summaries of General Visual Concepts
"... This paper considers the problem of selecting iconic images to summarize general visual categories. We define iconic images as high-quality representatives of a large group of images consistent both in appearance and semantics. To find such groups, we perform joint clustering in the space of global ..."
Abstract
-
Cited by 17 (3 self)
- Add to MetaCart
(Show Context)
This paper considers the problem of selecting iconic images to summarize general visual categories. We define iconic images as high-quality representatives of a large group of images consistent both in appearance and semantics. To find such groups, we perform joint clustering in the space of global image descriptors and latent topic vectors of tags associated with the images. To select the representative iconic images for the joint clusters, we use a quality ranking learned from a large collection of labeled images. For the purposes of visualization, iconic images are grouped by semantic “theme ” and multidimensional scaling is used to compute a 2D layout that reflects the relationships between the themes. Results on four large-scale datasets demonstrate the ability of our approach to discover plausible themes and recurring visual motifs for challenging abstract concepts such as “love ” and “beauty.” 1.
