ABSTRACT: In this paper a new method for image retrieval using high level color semantic features is proposed. It is based on extraction of low level color characteristics and their conversion into high level semantic features using Johannes Itten theory of color,

In this paper we propose a new General Image DataBase (GIDB) model. The model establishes taxonomy based on the systematisation of existing approaches. The GIDB model is based on the General Image Data model [1] and General Image Retrieval model [2]. The GIDB model uses the powerful features offered by objectoriented modelling, the elegance of the relational databases, the state of art of computer vision and the current methods for knowledge representation and management to achieve effective image retrieval. The developed language for the model is a hybrid between interactive and descriptive query languages. The ideas of the model can be used in the design of image retrieval libraries for an object-oriented database. As an illustration the results of applying the GIDB model to a plant database in the Sofia Image Database Management System are presented.

In this paper a new method for image retrieval using high level semantic features is proposed. It is based on extraction of low level color, shape and texture characteristics and their conversion into high level semantic features using fuzzy production rules, derived with the help of an image mining technique. DempsterShafer theory of evidence is applied to obtain a list of structures containing information for the image high level semantic features. Johannes Itten theory is adopted for acquiring high level color features.

The many successful research results in the domain of computer vision have made similarity-based data retrieval techniques a promising approach. As a result, the integration of similarity-based retrieval techniques of multimedia data in to DBMSs is currently an active research issue. In this paper, we rst illustrate the importance of similarity-based operations. Then, we present our image data repository model that supports similarity-based operations conveniently under an object-relational database paradigm. Furthermore, we present novel similarity-based operators on image tables and study their properties. Finally, based on the properties of the operators identied, we derive algebraic rules that are useful for similarity-based query optimization and will introduce a cost model for an implementation of one of the major similarity based operators. 1. Motivation The use of low-level contents of multimedia data for its identication, storage and operation purpose has been one of the major issues of research in the last decade. As a result, a number of research prototypes, applications, and commercial systems that support low-level content manipulation have been developed [17, 3, 18, 14, 19]. These works practically demonstrated that, the need for an automatic extraction, classication, and manipulation of the content of multimedia data is of critical signicance for an ecient multimedia data management. The problem of extracting the content of an image and identifying the proper techniques for comparing whether two images are alike or not is more of a work in the eld of visual recognition. In this paper, we focus on the management of content-based 1 image databases. We considered images, because image data is the most common and the widely used media data. Moreover, ...

In this paper we present image data representation, similarity image retrieval, the architecture of a generic content-based image retrieval system, and different content-based image retrieval systems.

Abstract -Content Based Image Retrieval (CBIR) is becoming effective source of fast retrieval on Image database for current internet world as it contains large collections of images. Content Based Image Retrieval (CBIR) is a technique which uses visual features of image such as color, shape, texture, etc. to search user required image from large image database according to user's requests in the form of a query image. Images are retrieved on the basis of similarity in features where features of the query specification are compared with features from the image database to determine which images match similarly with given features. Feature extraction is a crucial part for any of such retrieval systems. In this paper we survey some technical aspects of current content-based image retrieval systems and the features extraction techniques like color histogram, texture, and shape is done. Also paper gives the comparative analysis of mentioned techniques with different metrics.

Abstract: Managing image data in a database system using metadata has been practiced since the last two decades. However, describing an image fully and adequately with metadata is practically not possible. The other alternative is describing image content by its low-level features such as color, texture, shape, etc. and using the same for similarity-based image retrieval. However, practice has shown that using only the low-level features can not as well be complete. Hence, systems need to integrate both low-level and metadata descriptions for an efficient image data management. However, due to lack of adequate image data model, absence of a formal algebra for content-based image operations, and lack of precision of the existing image processing and retrieval techniques, no much work is done to integrate the use of lowlevel and metadata description and retrieval methods. In this paper, we first present a global image data model that supports both metadata and low-level descriptions of images and their salient objects. This allows to make multi-criteria image retrieval (context-, semantic-, and content-based queries). Furthermore, we present an image data repository model that captures all data described in the model and permits to integrate heterogeneous operations in a DBMS. In particular, similarity-based operations (similarity-based join and selection) in combination with traditional ones can be carried out. Finally, we present an image DBMS architecture that we use to develop a prototype in order to support both content-based and metadata retrieval.

Abstract: Part of network management is collecting information about the activities that go on around a distributed system and analyzing it in real time, at a deferred moment, or both. The reason such information may be stored in log files and analyzed later is to data-mine it so that interesting, unusual, or abnormal patterns can be discovered. In this paper we propose defining patterns in network activity logs using a dialect of First Order Temporal Logics (FOTL), called First Order Temporal Logic with Duration Constrains (FOTLDC). This logic is powerful enough to describe most network activity patterns because it can handle both causal and temporal correlations. Existing results for data-mining patterns with similar structure give us the confidence that discovering DFOTL patterns in network activity logs can be done efficiently.

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A number of formal models for images [13,27,28] and models for text and image matching [1] have been proposed, but they have not sufficiently dealt with features with high-level semantics. While formal models are supposed to be precise, their structures should allow for the level of subjectivity involved in interpreting the high-level semantics inherent in images. In our earlier work, we have shown that by restricting image retrieval to a specific domain, we can use logical reasoning based on common sense knowledge bases and the knowledge extracted from text corpora from the same domain to infer higher level semantics from lower level semantics. The interpretation of these lower level semantics, usually involving objects in the image, is subject to a lower level of subjectivity, hence making it possible to build an image model that is reasonably objective. Based on these observations, we propose that an effective and feasible approach to build high-level semantics into image retrieval is to build semantic models for both the image (the object of meaning) and image understanding (the perception of meaning). The image model will aim to capture image features which are commonly accepted within a certain domain. The image understanding model will include mechanisms for subjective interpretation and will be associated with correspondence functions which measure similarity between instances of these two models. This level of similarity, or the semantic distance, can be called the semiotic gap. Using this framework, the image retrieval problem can be deemed equivalent to the problem of defining a correspondence function that delivers the theoretically, or empirically, narrowest semiotic gap. We propose to construct the formal image model based on the concepts of semiotic structures, and an image understanding model based upon insights into how knowledge inference could assist with image retrieval. In this paper, we present the formal image model and argue why this model is suitable for the retrieval of visual data. An image understanding model, which is under ongoing research, is also briefly discussed with results of some preliminary experiments.