44,388 research outputs found
ImageSpirit: Verbal Guided Image Parsing
Humans describe images in terms of nouns and adjectives while algorithms
operate on images represented as sets of pixels. Bridging this gap between how
humans would like to access images versus their typical representation is the
goal of image parsing, which involves assigning object and attribute labels to
pixel. In this paper we propose treating nouns as object labels and adjectives
as visual attribute labels. This allows us to formulate the image parsing
problem as one of jointly estimating per-pixel object and attribute labels from
a set of training images. We propose an efficient (interactive time) solution.
Using the extracted labels as handles, our system empowers a user to verbally
refine the results. This enables hands-free parsing of an image into pixel-wise
object/attribute labels that correspond to human semantics. Verbally selecting
objects of interests enables a novel and natural interaction modality that can
possibly be used to interact with new generation devices (e.g. smart phones,
Google Glass, living room devices). We demonstrate our system on a large number
of real-world images with varying complexity. To help understand the tradeoffs
compared to traditional mouse based interactions, results are reported for both
a large scale quantitative evaluation and a user study.Comment: http://mmcheng.net/imagespirit
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
We introduce a model for bidirectional retrieval of images and sentences
through a multi-modal embedding of visual and natural language data. Unlike
previous models that directly map images or sentences into a common embedding
space, our model works on a finer level and embeds fragments of images
(objects) and fragments of sentences (typed dependency tree relations) into a
common space. In addition to a ranking objective seen in previous work, this
allows us to add a new fragment alignment objective that learns to directly
associate these fragments across modalities. Extensive experimental evaluation
shows that reasoning on both the global level of images and sentences and the
finer level of their respective fragments significantly improves performance on
image-sentence retrieval tasks. Additionally, our model provides interpretable
predictions since the inferred inter-modal fragment alignment is explicit
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Video summarisation: A conceptual framework and survey of the state of the art
This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users
Show and Tell: A Neural Image Caption Generator
Automatically describing the content of an image is a fundamental problem in
artificial intelligence that connects computer vision and natural language
processing. In this paper, we present a generative model based on a deep
recurrent architecture that combines recent advances in computer vision and
machine translation and that can be used to generate natural sentences
describing an image. The model is trained to maximize the likelihood of the
target description sentence given the training image. Experiments on several
datasets show the accuracy of the model and the fluency of the language it
learns solely from image descriptions. Our model is often quite accurate, which
we verify both qualitatively and quantitatively. For instance, while the
current state-of-the-art BLEU-1 score (the higher the better) on the Pascal
dataset is 25, our approach yields 59, to be compared to human performance
around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66,
and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we
achieve a BLEU-4 of 27.7, which is the current state-of-the-art
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following findings
in cognitive psychology, our model is composed of layers representing maps at different levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
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