The obstacle of generating hybrid queries within the context of content-based image retrieval is still very real. In attempts to overcome this, fuzzy aggregation can be used to combine single, simple index queries into larger, more complex ones. This paper outlines the use of a fuzzy aggregation technique for hybrid querying which has the ability to adjust its behavior according operator-controlled parameters. The resulting aggregator can be viewed as a featureadaptive overall similarity measure. For the purposes of this extended summary, the scope of the aggregator is limited to queries involving color content, color coverage, and horizontal /vertical trends, and applied to a media database comprised of COREL images of fixed size. Preliminary results show promise and illustrate that hybrid queries using the aforementioned fuzzy aggregator are effective in their ability to retrieve relevant images while suppressing erroneous retrievals when compared to simple, single-feature queries. In addition, the results obtained are at a minimum compara- ble to multiple-feature queries generated using a weighted mean approach but exhibiting scalability and greater fiexir bility in parameter adjustment.
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[Show abstract][Hide abstract] ABSTRACT: Color image histograms are very useful tools for content based image retrieval (CBIR) that can be applied on features such as colour, texture and shape. As these kinds of histograms results with large variations between neighbouring bins, they seem so sensitive to any kind of changes such as noise, illumination. To overcome this problem, in this paper, fuzzy linking histogram approach based on OWA aggregation operator is proposed, which is capable of projecting 3-dimensional (L*a*b*) colour histograms into single-dimension. The proposed method have been evaluated and compared with five other related methods in retrieving similar images from the common dataset which is available on http://utopia.duth.gr/~konkonst. The experimental results on 100 images within two categories of Cat and Sky reveals better performance of the proposed method in comparison with the other mentioned methods.
Computational Cybernetics, 2009. ICCC 2009. IEEE International Conference on; 03/2009
[Show abstract][Hide abstract] ABSTRACT: The performance of image retrieval (IR) systems improves by reducing the semantic gap between the low-level features and the high-level concepts. Research results in the recent years show that combining the two modalities (text based and content based) even with simple fusion strategies alleviates the image retrieval results and also reduces the semantic gap. In this paper, we propose a new approach called weighted semantic similarity, which assesses the semantics between the query image and textual query provided by the user as an input to the system. The similarity between the keywords has been measured the using WordNet. For content matching, color feature is extracted and is represented using Fuzzy Color Histogram (FCH). The two modalities are fused together using reordering technique to improve the retrieval results. The proposed approach shows that the semantics learned at an early stage not only reduces the semantic gap but also decreases the computation time largely. The Mean Average precision (MAP) of 0.4311 is achieved using the proposed approach.
Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on; 08/2013