With the rapidly growing number of images over the Internet, efficient scalable semantic image retrieval becomes increasingly\nimportant. This paper presents a novel approach for semantic image retrieval by combining Convolutional Neural Network (CNN)\nand Markov Random Field (MRF). As a key step, image concept detection, that is, automatically recognizing multiple semantic\nconcepts in an unlabeled image, plays an important role in semantic image retrieval. Unlike previous work that uses single-concept\nclassifiers one by one, we detect semanticmulticoncept by using amulticoncept scene classifier. In other words, our approach takes\nmultiple concepts as a holistic scene formulticoncept scene learning. Specifically, we first train a CNN as a concept classifier, which\nfurther includes two types of classifiers: a single-concept fully connected classifier that is best suited to single-concept detection and\na multiconcept scene fully connected classifier that is good for holistic scene detection.Then we propose an MRF-based late fusion\napproach that is able to effectively learn the semantic correlation between the single-concept classifier and multiconcept scene\nclassifier. Finally, the semantic correlation among the subconcepts of images is cought to further improve detection precision. In\norder to investigate the feasibility and effectiveness of our proposed approach, we conduct comprehensive experiments on two\npublicly available image databases.The results show that our proposed approach outperforms several state-of-the-art approaches.
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