An Interactive Approach for Retrieval of Semantically Significant Images
Pranoti P. Mane, Narendra G. Bawane
Journal of Multimedia Processing and Technologies, Vol. 8, No. 4, 2017.
DOI: 
Link: http://www.dline.info/jmpt/fulltext/v8n4/jmptv8n4_1.pdf
Abstract:
Content-based image retrieval is the process of recovering the images that are based on their primitive features
such as texture, color, shape etc. The main challenge in this type of retrieval is the gap between low-level primitive features and
high-level semantic concepts. This is known as the semantic gap. This paper proposes an interactive approach for optimizing
the semantic gap. The primitive features used are HSV histogram, local binary pattern histogram, and color coherence vector
histogram. The mapping between primitive features of the image and its semantic concepts is done by involving the user in the
feedback loop. Proposed primitive feature extraction method shows improved image retrieval results (Average precision
73.1%) over existing methods. We have proposed an innovative relevance feedback technique in which the concept of prominent
features is introduced. On the application of the relevance feedback, only prominent features which are having maximum
similarity are utilized. This method reduces the feature length and increases the efficiency. Our own interactive approach for
relevance feedback is not only computationally simple and fast but also shows improvement in the retrieval of semantically
meaningful relevant images as we go on increasing the iterations.