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Exploiting visual word co-occurrence for image retrieval
2012
Proceedings of the 20th ACM international conference on Multimedia - MM '12
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A co-occurrence matrix is introduced to refine the similarity measure for image ranking. ...
In this paper, we construct a visual word co-occurrence table by exploring visual word co-occurrence extracted from small affine-invariant regions in a large collection of natural images. ...
CONCLUSIONS This paper has proposed a novel image retrieval approach that exploits the spatial co-occurrence of visual words. ...
doi:10.1145/2393347.2393364
dblp:conf/mm/ShiSTX12
fatcat:kcv53roeqbahzljcbnaudhjrsq
Exploring Spatial Correlation for Visual Object Retrieval
2015
ACM Transactions on Intelligent Systems and Technology
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Subsequently, we propose two methods of co-occurrence weighting similarity measure for image ranking: Co-Cosine and Co-TFIDF. ...
This paper outlines the construction of a visual word co-occurrence matrix by exploring visual word co-occurrence extracted from small affine-invariant regions in a large collection of natural images. ...
In comparison, our method only exploits the spatial co-occurrence of visual words in the entire dataset. ...
doi:10.1145/2641576
fatcat:jibn2tavdjezhbrr6wzdvy2x6e
Toward a higher-level visual representation for object-based image retrieval
2008
The Visual Computer
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We propose a higher-level visual representation, visual synset, for object-based image retrieval beyond visual appearances. ...
First, the approach strengthens the discrimination power of visual words by constructing an intermediate descriptor, visual phrase, from frequently co-occurring visual word-set. ...
Phase 2 tackles the low discrimination issue in visual words, by exploiting the spatial co-occurrence information among visual words. ...
doi:10.1007/s00371-008-0294-0
fatcat:mtmoj5gysrb43e32jffbcvdsfy
Integrating visual and semantic contexts for topic network generation and word sense disambiguation
2009
Proceeding of the ACM International Conference on Image and Video Retrieval - CIVR '09
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for addressing the issues of polysemes and synonyms more effectively, thus it can significantly improve the precision and recall rates for image retrieval. ...
Second, our word sense disambiguation algorithm can integrate the topic network to exploit both the visual similarity contexts between the images and the semantic similarity contexts between their tags ...
co-occurrence probability. ...
doi:10.1145/1646396.1646440
dblp:conf/civr/FanLSY09
fatcat:dvczr67ddnenjhp3w3reurc25u
Improving Content-Based Image Retrieval by Identifying Least and Most Correlated Visual Words
[chapter]
2012
Lecture Notes in Computer Science
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In this paper, we propose a model for direct incorporation of image content into a (short-term) user profile based on correlations between visual words and adaptation of the similarity measure. ...
The information about the most and the least correlated visual words can be exploited in order to adapt the similarity measure. ...
Liu et. al. ([6] ) exploit co-occurrence information in spatial domain. Authors make an assumption that the related visual words would appear in a certain neighbourhood. ...
doi:10.1007/978-3-642-35341-3_27
fatcat:m5tfbwy6rbajhnx6jc5xx4he5y
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
[article]
2021
arXiv
pre-print
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Specifically, the consensus information is exploited by computing the statistical co-occurrence correlations between the semantic concepts from the image captioning corpus and deploying the constructed ...
Afterwards, CVSE learns the associations and alignments between image and text based on the exploited consensus as well as the instance-level representations for both modalities. ...
With the instantiated concepts, their co-occurrence relationship are examined to build one correlation graph and thereby exploit the commonsense knowledge. ...
arXiv:2007.08883v2
fatcat:xw4ktscovvdz3fuhe3vkjstbie
Combining Multi-modal Features for Social Media Analysis
[chapter]
2011
Social Media Modeling and Computing
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Tagged images taken from social sites have been used in the characteristic scenarios of image clustering and retrieval, to demonstrate the benefits of multi-modal analysis in social media. ...
In this chapter we discuss methods for efficiently modeling the diverse information carried by social media. ...
For all images the authors released 500-dimensional co-occurrence vectors for visual words (as described in Section 4.2.1), as well as 1000-dimensional co-occurrence vectors for tag-words (as described ...
doi:10.1007/978-0-85729-436-4_4
fatcat:c5rsc2fi5zcglcmgtvsa5b6fca
Multimodal Distributional Semantics
2014
The Journal of Artificial Intelligence Research
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of a word can be extended to also encompass its co-occurrence with the visual words of images it is associated with. ...
Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. ...
In particular, our multimodal distributional semantic model (MDSM) exploits both co-occurrence with words (from a standard text corpus) and co-occurrence with visual features extracted using computer vision ...
doi:10.1613/jair.4135
fatcat:hwiocijrfbfdbhufc26eyaxgim
Automatic Image Annotation Using Maximum Entropy Model
[chapter]
2005
Lecture Notes in Computer Science
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Automatic image annotation is a newly developed and promising technique to provide semantic image retrieval via text descriptions. ...
It concerns a process of automatically labeling the image contents with a pre-defined set of keywords which are exploited to represent the image semantics. ...
By exploiting text and image feature co-occurrence statistics, these methods can extract hidden semantics from images, and have been proven successful in constructing a nice framework for the domain of ...
doi:10.1007/11562214_4
fatcat:4mkfcthddbcdfchkvsxynczywi
A Bayesian Approach to Hybrid Image Retrieval
[chapter]
2009
Lecture Notes in Computer Science
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Content based image retrieval (CBIR) has been well studied in the computer vision and multimedia community. ...
We fuse both of them in a Bayesian framework to design a hybrid image retrieval system by overcoming their shortcomings. ...
On contrary, CFIR methods exploit the co-occurrence information (for example in a collaborative filtering framework) in the logs of image-access to model the similarity across images [3, 5] . ...
doi:10.1007/978-3-642-11164-8_79
fatcat:ja6c64prxzgzxoip52xykgooji
High order pLSA for indexing tagged images
2013
Signal Processing
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This work presents a method for the efficient indexing of tagged images. Tagged images are a common resource of social networks and occupy a large portion of the social media stream. ...
Their basic characteristic is the co-existence of two heterogeneous information modalities, i.e. visual and tag, which refer to the same abstract meaning. ...
For all images we have used the 500-dimensional co-occurrence vectors for visual words and the 1000-dimensional cooccurrence vectors for tag-words that were released by the authors. ...
doi:10.1016/j.sigpro.2012.08.004
fatcat:2bacd2atq5eivn6ths6ehftzc4
Semantic-visual concept relatedness and co-occurrences for image retrieval
2012
2012 19th IEEE International Conference on Image Processing
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Third, we leverage the visual and semantic correspondence and the co-occurrence patterns to improve the accuracy and efficiency for image retrieval. ...
This paper introduces a novel approach that allows the retrieval of complex images by integrating visual and semantic concepts. The basic idea consists of three aspects. ...
Therefore, the measure from concept co-occurrence could be more important. The main contributions of this paper is to uncover concept co-occurrence patterns for image retrieval. ...
doi:10.1109/icip.2012.6467388
dblp:conf/icip/FengB12
fatcat:e3su7ccs3vdljnkcq4ytulv5de
Negative Evidences and Co-occurences in Image Retrieval: The Benefit of PCA and Whitening
[chapter]
2012
Lecture Notes in Computer Science
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and the co-occurrences phenomenon. ...
We show and explicitly exploit relations between i) mean subtraction and the negative evidence, i.e., a visual word that is mutually missing in two descriptions being compared, and ii) the axis de-correlation ...
The paper is organized as follows: After introducing the context in Section 2, Section 3 shows the role of co-missing visual words and Section 4 exploits whitening to address the issue arising with co-occurrence ...
doi:10.1007/978-3-642-33709-3_55
fatcat:xqajeeyjo5f6td4pysnz3zlun4
Multimodal pLSA on visual features and tags
2009
2009 IEEE International Conference on Multimedia and Expo
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This work studies a new approach for image retrieval on largescale community databases. ...
We use topic models to derive a high-level representation appropriate for retrieval for each of our images in the database. ...
The occurrences of visual words in an image are hereby counted into a co-occurrence vector, also called document vector. ...
doi:10.1109/icme.2009.5202522
dblp:conf/icmcs/RombergHL09
fatcat:kfremgkw3bgq3kn3mriwutqa3q
Quadtree decomposition based extended vector space model for image retrieval
2011
2011 IEEE Workshop on Applications of Computer Vision (WACV)
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Bag of visual words approach for image retrieval does not exploit the spatial distribution of visual words in an image. ...
This paper proposes a novel extended vector space based image retrieval technique which takes into account the spatial occurrence (context) of a visual word in an image along with the co-occurrence of ...
Hence, comparative results with SPM based scheme are provided to evaluate our extended vector space approach for image retrieval based on spatial distribution and co-occurrences of visual words. ...
doi:10.1109/wacv.2011.5711495
dblp:conf/wacv/RamanathanMM11
fatcat:dojp7bnwc5dfzaqzbw6j4pbm2m
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