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Multi-scale image semantic recognition with hierarchical visual vocabulary
2011
Computer Science and Information Systems
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The vocabulary is used to recognize the image semantic. In this paper, a new scheme to construct semantic-binding hierarchical visual vocabulary is proposed. ...
Some attributes and relationship of the semantic nodes in the model are discussed. The hierarchical semantic model is used to organize the multi-scale semantic into a level-by-level structure. ...
Our model can help to understand and analyze image semantic in a flexible multi-resolution way, and get the semantic recognition on each semantic node. ...
doi:10.2298/csis100423035j
fatcat:3xqwn5qrobbp7hptbc5mrehm2m
Semantic hierarchies for image annotation: A survey
2012
Pattern Recognition
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In this survey, we argue that using structured vocabularies is capital to the success of image annotation. ...
We analyze literature on image annotation uses and user needs, and we stress the need for automatic annotation. ...
Visual recognition in the human brain consists in linking the image printed on the retina with a representation stored in memory [92] . ...
doi:10.1016/j.patcog.2011.05.017
fatcat:hoyo5p2oyfff3lvu6yi24vbcwi
Visual Vocabulary Learning and Its Application to 3D and Mobile Visual Search
[article]
2012
arXiv
pre-print
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In this technical report, we review related works and recent trends in visual vocabulary based web image search, object recognition, mobile visual search, and 3D object retrieval. ...
Especial focuses would be also given for the recent trends in supervised/unsupervised vocabulary optimization, compact descriptor for visual search, as well as in multi-view based 3D object representation ...
City-Scale Landmark Search Towards city-scale landmark search and recognition, Schindler et al. ...
arXiv:1207.7244v1
fatcat:b6y7yvvcu5davkwh3zuv762qq4
Semantics Extraction from Images
[chapter]
2011
Lecture Notes in Computer Science
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An overview of the state-of-the-art on semantics extraction from images is presented. ...
Knowledge can be represented in either implicit or explicit fashion while the image is represented in different levels, namely, low-level, intermediate and semantic level. ...
A multi-scale representation is achieved by performing image segmentation at three image scales (as in [66] ), assigning three different regions to each pixel. ...
doi:10.1007/978-3-642-20795-2_3
fatcat:h3xh2fmryfgj5hwn5k5fjsximy
GroupViT: Semantic Segmentation Emerges from Text Supervision
[article]
2022
arXiv
pre-print
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We train GroupViT jointly with a text encoder on a large-scale image-text dataset via contrastive losses. ...
Grouping and recognition are important components of visual scene understanding, e.g., for object detection and semantic segmentation. ...
By training on large-scale paired image-text data with contrastive losses, we enable the model to be zero-shot transferred to several semantic segmentation vocabularies, without requiring any further annotation ...
arXiv:2202.11094v3
fatcat:5agovy2yifecphcmoolwmhect4
Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera
2016
Sensors
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To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences ...
We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. ...
This framework can be used to link the elements in a visual vocabulary with the elements of a semantic vocabulary. ...
doi:10.3390/s16040493
pmid:27070607
pmcid:PMC4851007
fatcat:svbsehelw5fdthws5uwgdedxse
Semantic-Aware Co-Indexing for Image Retrieval
2015
IEEE Transactions on Pattern Analysis and Machine Intelligence
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are robust to delineate low-level image contents, and 2) semantic attributes from large-scale object recognition that may reveal image semantic meanings. ...
In this paper, for vocabulary tree based image retrieval, we propose a semantic-aware co-indexing algorithm to jointly embed two strong cues into the inverted indexes: 1) local invariant features that ...
K
SIFT descriptors [13] of dimension D = 128, so does {x j } j∈S d for d.A visual vocabulary tree T is obtained by hierarchical Offline
Indexing
Dense HOG/
LBP
Sparse SIFT
Coding of BoW
Multi-class ...
doi:10.1109/tpami.2015.2417573
pmid:26539859
fatcat:2adewvpkijeq3f6dhbjvbic54u
Semantic-Aware Co-indexing for Image Retrieval
2013
2013 IEEE International Conference on Computer Vision
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are robust to delineate low-level image contents, and 2) semantic attributes from large-scale object recognition that may reveal image semantic meanings. ...
In this paper, for vocabulary tree based image retrieval, we propose a semantic-aware co-indexing algorithm to jointly embed two strong cues into the inverted indexes: 1) local invariant features that ...
K
SIFT descriptors [13] of dimension D = 128, so does {x j } j∈S d for d.A visual vocabulary tree T is obtained by hierarchical Offline
Indexing
Dense HOG/
LBP
Sparse SIFT
Coding of BoW
Multi-class ...
doi:10.1109/iccv.2013.210
dblp:conf/iccv/ZhangYWLT13
fatcat:7b7mzbj4fnbtffi3ierfnkystu
Spatial pyramid local keypoints quantization for bag of visual patches image representation
2010
2010 10th International Conference on Intelligent Systems Design and Applications
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We show, with experiments on multi-class classification task using 700 natural scene images, that the spatial pyramid vocabulary model is suitable and discriminative for bag-of-visual patches semantic ...
Bag of visual patches (BOP) image representation has been the main research topic in computer vision literature for scene and object recognition tasks. ...
We only experiment with visual vocabulary of size 200 in order to compare with Lazebnik et al. approach [27] . ...
doi:10.1109/isda.2010.5687083
dblp:conf/isda/AlqasrawiNC10
fatcat:o376oiiqwvelppxmbtnayos2p4
Weakly supervised codebook learning by iterative label propagation with graph quantization
2013
Signal Processing
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Visual codebook serves as a fundamental component in many state-of-the-art visual search and object recognition systems. ...
groups similar patches with related labels (modeled by WordNet [18]), which minimizes the visual distortions in quantization. ...
errors [24] , because it quantizes separately at each scale, with multi-to-multi linking graph to connect hierarchical codewords. (2) Different from all previous works [9] [10] [11] [12] [13] 16, 17 ...
doi:10.1016/j.sigpro.2012.05.001
fatcat:d47fymh4ircdbeiyl2gpqu4bx4
Learning semantic features for action recognition via diffusion maps
2012
Computer Vision and Image Understanding
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As opposed to flat vocabularies used in traditional methods, we propose to exploit the hierarchical nature of feature vocabularies representative of human actions. ...
We present a principled approach to learning a semantic vocabulary from a large amount of video words using diffusion maps embedding. ...
[37] proposed unifying the vocabulary construction with classifier training, and then encoding an image by a sequence of visual bits that constitute the semantic vocabulary. ...
doi:10.1016/j.cviu.2011.08.010
fatcat:u3glpakfsrazdlzuygpgst2i2a
Semantic Hierarchies for Visual Object Recognition
2007
2007 IEEE Conference on Computer Vision and Pattern Recognition
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We use the semantics of image labels to integrate prior knowledge about inter-class relationships into the visual appearance learning. ...
We also demonstrate additional features that become available to object recognition due to the extension with semantic inference tools-we can classify high-level categories, such as animals, and we can ...
Given a vocabulary, we can represent each image in the dataset as a histogram of visual words [16] . ...
doi:10.1109/cvpr.2007.383272
dblp:conf/cvpr/MarszalekS07a
fatcat:d4rvtb5zlzac3k4g5j4aei5v5i
Open Vocabulary Scene Parsing
2017
2017 IEEE International Conference on Computer Vision (ICCV)
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Our approach is a joint image pixel and word concept embeddings framework, where word concepts are connected by semantic relations. ...
In this paper, we propose a new task that aims at parsing scenes with a large and open vocabulary, and several evaluation metrics are explored for this problem. ...
Recent efforts in image classification/detection/segmentation have shown this trend: emerging image datasets enable recognition on a large scale [6, 30, 32] , while image captioning can be seen as a special ...
doi:10.1109/iccv.2017.221
dblp:conf/iccv/ZhaoPZF017
fatcat:flambms24bhlrjpzms2hao4g2e
Hierarchical Visual Place Recognition Based on Semantic-Aggregation
2021
Applied Sciences
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Inspired by this, we propose a hierarchical visual place recognition pipeline based on semantic-aggregation and scene understanding for the images. ...
Semantic-aggregation happens in residual aggregation of visual information and semantic information in coarse matching, and semantic association of semantic edges in fine matching. ...
[11] proposed a visual vocabulary-based loop-closure method, where the visual vocabularies could be built online, enabling the bag-of-words model to adapt to the dynamically changing environments. ...
doi:10.3390/app11209540
fatcat:5srkg3yusrfazkmorijgszgf2q
Massive-scale multimedia semantic modeling
2013
Proceedings of the 21st ACM international conference on Multimedia - MM '13
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To address these challenges, this tutorial will provide a unified overview of the two emerging techniques: Semantic modeling and Massive scale visual recognition, with a goal of both introducing people ...
Visual data is exploding! 500 billion consumer photos are taken each year world-wide, 633 million photos taken per year in NYC alone. 120 new video-hours are uploaded on YouTube per minute. ...
Scale Visual Recognition Challenges, Im-ageCLEF recognition and TRECVID challenges. ...
doi:10.1145/2502081.2502235
dblp:conf/mm/SmithC13a
fatcat:gvhxnztvnvakpl4pmizqwox7di
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