2016
DOI: 10.1109/tpami.2015.2469281
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Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval

Abstract: Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence p… Show more

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Cited by 58 publications
(16 citation statements)
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References 61 publications
(90 reference statements)
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“…We can also find the semantic similarity between the predicted class labels. As there is high probability for semantically similar concepts to co-occur in an image [13], for example 'cars' and 'trucks'. The semantic similarity score can help in accurately labelling images.…”
Section: Test Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…We can also find the semantic similarity between the predicted class labels. As there is high probability for semantically similar concepts to co-occur in an image [13], for example 'cars' and 'trucks'. The semantic similarity score can help in accurately labelling images.…”
Section: Test Resultsmentioning
confidence: 99%
“…The proposed model using discrete cosine transform (DCT) for feature extraction. In 2016, Linan Feng proposed a system for image retrieval and annotation based on semantic concept co-occurrence [13]. In his paper, he describes a new approach to automatically generate descriptions for the images by considering concept co-occurrence patterns in the pre-labelled training dataset that makes it possible to create complex semantic descriptions for scene images.…”
Section: Related Workmentioning
confidence: 99%
“…A good survey on concept-based video retrieval is presented by Snoek and Worring [2]. Feng and Bhanu [3] discussed the concept and use of concept co-occurrence patterns for image annotation and retrieval. Kuo et al [4] presented the work on the use of deep convolutional neural network for image retrieval.…”
Section: Related Workmentioning
confidence: 99%
“…Motivated by the fact that multiple concepts that frequently co-occur over images form patterns that could provide contextual cues for proper concept inference, the researchers in [28] outlined an AIA approach that exploited the co-occurrence patterns as hierarchical communities in the pre-labelled training set. In addition, the model called automatic linguistic indexing of pictures (ALIP) [29] was used to compute the likelihood of the occurrence of images based on a describing stochastic process.…”
Section: Related Workmentioning
confidence: 99%