Abstract: The system is designed to show images which are
related to the query image. Extracting color, texture, and shape
features from an image plays a vital role in content-based image
retrieval (CBIR). Initially RGB image is converted into HSV color
space due to its perceptual uniformity. From the HSV image, Color
features are extracted using block color histogram, texture features
using Haar transform and shape feature using Fuzzy C-means
Algorithm. Then, the characteristics of the global and local color
histogram, texture features through co-occurrence matrix and Haar
wavelet transform and shape are compared and analyzed for CBIR.
Finally, the best method of each feature is fused during similarity
measure to improve image retrieval effectiveness and accuracy.
Abstract: This paper presents content-based image retrieval (CBIR) frameworks with relevance feedback (RF) based on combined learning of support vector machines (SVM) and AdaBoosts. The framework incorporates only most relevant images obtained from both the learning algorithm. To speed up the system, it removes irrelevant images from the database, which are returned from SVM learner. It is the key to achieve the effective retrieval performance in terms of time and accuracy. The experimental results show that this framework had significant improvement in retrieval effectiveness, which can finally improve the retrieval performance.
Abstract: Documents retrieval in Information Retrieval
Systems (IRS) is generally about understanding of
information in the documents concern. The more the system
able to understand the contents of documents the more
effective will be the retrieval outcomes. But understanding of the
contents is a very complex task. Conventional IRS apply algorithms
that can only approximate the meaning of document contents through
keywords approach using vector space model. Keywords may be
unstemmed or stemmed. When keywords are stemmed and conflated
in retrieving process, we are a step forwards in applying semantic
technology in IRS. Word stemming is a process in morphological
analysis under natural language processing, before syntactic and
semantic analysis. We have developed algorithms for Malay and
Arabic and incorporated stemming in our experimental systems in
order to measure retrieval effectiveness. The results have shown that
the retrieval effectiveness has increased when stemming is used in
the systems.
Abstract: Phrases has a long history in information retrieval, particularly in commercial systems. Implicit semantic relationship between words in a form of BaseNP have shown significant improvement in term of precision in many IR studies. Our research focuses on linguistic phrases which is language dependent. Our results show that using BaseNP can improve performance although above 62% of words formation in Malay Language based on derivational affixes and suffixes.