Structured Knowledge Representation for Image Retrieval

Sciascio, Eugenio Di; Donini, Francesco M.; Mongiello, Marina

doi:10.1613/jair.902

Cited by 23 publications

(11 citation statements)

References 47 publications

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“…In [28] a method starting from low-level features and creating a semantic representation of the images is presented and in [29] an approach to consistently fuse the efforts in various fields of multimedia information retrieval is presented.…”

Section: State Of the Art In Content-based Image Retrievalmentioning

confidence: 99%

Features for image retrieval: an experimental comparison

2007

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An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented. Many of the papers describing new techniques and descriptors for content-based image retrieval describe their newly proposed methods as most appropriate without giving an in-depth comparison with all methods that were proposed earlier. In this paper , we first give an overview of a large variety of features for content-based image retrieval and compare them quantitatively on four different tasks: stock photo retrieval, personal photo collection retrieval, building retrieval, and medical image retrieval. For the experiments, five different, publicly available image databases are used and the retrieval performance of the features is analysed in detail. This allows for a direct comparison of all features considered in this work and furthermore will allow a comparison of newly proposed features to these in the future. Additionally, the correlation of the features is analysed, which opens the way for a simple and intuitive method to find an initial set of suitable features for a new task. The article concludes with recommendations which features perform well for what type of data. Interestingly, the often used, but very simple, colour histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications.

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Section: State Of the Art In Content-based Image Retrievalmentioning

confidence: 99%

Features for image retrieval: an experimental comparison

2007

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“…Three other parts of the ontology contain position, orientation and size concepts. A formalization of a similar approach based on a combination o geometric shapes can be found in [12]. The size of an object can also be described and quantified with a set of quantifiers.…”

Section: Spatial Conceptsmentioning

confidence: 99%

Ontology based complex object recognition

Maillot

Thonnat

2008

Image and Vision Computing

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This paper presents a new approach for object categorization involving the following aspects of cognitive vision: learning, recognition and knowledge representation. A major element of our approach is a visual concept ontology composed of several types of concepts (spatial concepts and relations, color concepts and texture concepts). Visual concepts contained in this ontology can be seen as an intermediate layer between domain knowledge and image processing procedures. Machine learning techniques are used to solve the symbol grounding problem (i.e. linking meaningfully symbols to sensory information). This paper shows, how a new object categorization system is set up by a knowledge acquisition and learning phase and then used by an object categorization phase.

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“…Wong et al (1998) described a hidden Markov model capable of detecting abnormalities in system operation that could be used as a monitoring system for industrial plants, once again highlighting the need for a synthesis between observation and interaction in any functioning AI system. In a detailed paper, Di Sciascio et al (2002) discussed the problems of retrieving objects from within complex images, without relying on low-level characteristics such as colour distribution or texture. With faces, low-level characteristics may not be enough to allow correct recognition.…”

Section: Commercial and Industrialmentioning

confidence: 99%

The state of play in machine/environment interactions

Aitkenhead

McDonald

2006

Artif Intell Rev

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Due to the breadth of the subject, it is no longer possible to provide a review of all of the work being carried out in the field of Artificial Intelligence. However, a more localised review of research taking place in the overlap between engineering, AI and psychology can be meaningfully performed. We show here that while there have been marked successes in the past few years, there is an identifiable set of 'classic' problems that remain to be solved, and which largely direct the work ongoing in this area. This review aims to discuss the directions being taken at the current time, in particular the developing and maturing possibilities provided by neural networks and evolutionary computation, and by the use of our knowledge of the mind in developing artificial agents capable of mimicking our abilities to interact with the environment.

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Structured Knowledge Representation for Image Retrieval

Cited by 23 publications

References 47 publications

Features for image retrieval: an experimental comparison

Features for image retrieval: an experimental comparison

Ontology based complex object recognition

The state of play in machine/environment interactions

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