HANOLISTIC: A Hierarchical Automatic Image Annotation System Using Holistic Approach

2009-06-25
Karadag, Ozge Oztimur
Yarman Vural, Fatoş Tunay
Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this study, we propose a novel approach for automatic image annotation problem, where the annotation is formulated as a multivariate mapping from a set of independent descriptor spaces, representing a whole image, to a set of words, representing class labels. For this purpose, a hierarchical annotation architecture, named as HANOLISTIC (Hierarchical Image Annotation System Using Holistic Approach), is defined with two layers. The first layer, called level-0 consists of annotators each of which is fed by a set of distinct descriptors, extracted from the whole image. This enables us to represent the image at each annotator by a different visual property of a descriptor. Since, we use the whole image, the problematic segmentation process is avoided. Training of each annotator is accomplished by a supervised learning paradigm, where each word is considered as a class label. Note that, this approach is slightly different then the classical training approaches, where each data has a unique label. In the proposed system, since each image has one or more annotating words, we assume that an image belongs to more than one class. The output of the level-0 annotators indicate the membership values of the words in the vocabulary, to belong an image. These membership values from each annotator is, then, aggregated at the second layer to obtain meta-level annotator. Finally, a set of words from the vocabulary is selected based on the ranking of the output of meta-level. The hierarchical annotation system proposed in this study outperforms state of the art annotation systems based on segmental and holistic approaches.
IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops

Suggestions

Hanolistic : a hierarchical automatic image annotation system using holistic approach
Öztimur, Özge; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2008)
Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this thesis, we propose a novel approach for automatic image annotation problem, where the annotation is formulated as a multivariate mapping from a set of independent descriptor spaces, representing a whole image, to a set of words, representing class labels. For this purpose, a hiera...
Image annotation with semi-supervised clustering
Sayar, Ahmet; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2009)
Image annotation is defined as generating a set of textual words for a given image, learning from the available training data consisting of visual image content and annotation words. Methods developed for image annotation usually make use of region clustering algorithms to quantize the visual information. Visual codebooks are generated from the region clusters of low level visual features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this th...
Novel refinement method for automatic image annotation systems
Demircioğlu, Erşan; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2011)
Image annotation could be defined as the process of assigning a set of content related words to the image. An automatic image annotation system constructs the relationship between words and low level visual descriptors, which are extracted from images and by using these relationships annotates a newly seen image. The high demand on image annotation requirement increases the need to automatic image annotation systems. However, performances of current annotation methods are far from practical usage. The most ...
DATA-DRIVEN IMAGE CAPTIONING WITH META-CLASS BASED RETRIEVAL
Kilickaya, Mert; Erdem, Erkut; Erdem, Aykut; İKİZLER CİNBİŞ, NAZLI; Çakıcı, Ruket (2014-04-25)
Automatic image captioning, the process cif producing a description for an image, is a very challenging problem which has only recently received interest from the computer vision and natural language processing communities. In this study, we present a novel data-driven image captioning strategy, which, for a given image, finds the most visually similar image in a large dataset of image-caption pairs and transfers its caption as the description of the input image. Our novelty lies in employing a recently' pr...
Optical flow based video frame segmentation and segment classification
Akpınar, Samet; Alpaslan, Ferda Nur; Department of Computer Engineering (2018)
Video information retrieval is a field of multimedia research enabling us to extract desired semantic information from video data. In content-based video information retrieval, visual content obtained from video scenes is utilized. For developing methods to cope with content-based video information retrieval in terms of temporal concepts such as action, event, etc., representation of temporal information becomes critical. In this thesis, action detection is tackled based on a temporal video representation m...
Citation Formats
O. O. Karadag and F. T. Yarman Vural, “HANOLISTIC: A Hierarchical Automatic Image Annotation System Using Holistic Approach,” presented at the IEEE-Computer-Society Conference on Computer Vision and Pattern Recognition Workshops, Miami, FL, USA, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55767.