Semantic visual search with feature space reduction

High dimensional visual features and multimodal based search systems are popular due to their visual retrieval accuracy. But high dimensionality and multimodality negatively affect efficiency and computational complexity. This paper brings the performance advantages of low dimensional visual feature called Compacted Dither Pattern Code (CDPC). CDPC is accompanied with texel properties, spatial colour arrangements, high compactness, and robust feature representation. CDPC feature uses a low computational cost in visual feature extraction which is illustrated using four abstract stages. This feature has proven its performances in our experimental study. In our experiment, CDPC was used alone as a syntactic visual feature for semantic visual depiction and retrieval. Ten irregular shapes based visual concepts were used in visual description. The average precision results generated from ranked retrieval set were used as a metric in evaluation. The retrieval result based on Trecvid 2005 dataset is presented compared to well known low dimensional and high dimensional visual features. Results show the balanced made by CDPC with Bhattacharyya classification in between retrieval accuracy and visual feature space reduction. Competitive advantages of this method over state of art systems are also technically described.