53,098 research outputs found
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval
In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low
discriminative power, so false positive matches occur prevalently. Apart from
the information loss during quantization, another cause is that the SIFT
feature only describes the local gradient distribution. To address this
problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform
feature fusion at indexing level. Basically, complementary features are coupled
into a multi-dimensional inverted index. Each dimension of c-MI corresponds to
one kind of feature, and the retrieval process votes for images similar in both
SIFT and other feature spaces. Specifically, we exploit the fusion of local
color feature into c-MI. While the precision of visual match is greatly
enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation
of SIFT and color features significantly reduces the impact of false positive
matches.
Extensive experiments on several benchmark datasets demonstrate that c-MI
improves the retrieval accuracy significantly, while consuming only half of the
query time compared to the baseline. Importantly, we show that c-MI is well
complementary to many prior techniques. Assembling these methods, we have
obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench
datasets, respectively, which compare favorably with the state-of-the-arts.Comment: 8 pages, 7 figures, 6 tables. Accepted to CVPR 201
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
A location-aware embedding technique for accurate landmark recognition
The current state of the research in landmark recognition highlights the good
accuracy which can be achieved by embedding techniques, such as Fisher vector
and VLAD. All these techniques do not exploit spatial information, i.e.
consider all the features and the corresponding descriptors without embedding
their location in the image. This paper presents a new variant of the
well-known VLAD (Vector of Locally Aggregated Descriptors) embedding technique
which accounts, at a certain degree, for the location of features. The driving
motivation comes from the observation that, usually, the most interesting part
of an image (e.g., the landmark to be recognized) is almost at the center of
the image, while the features at the borders are irrelevant features which do
no depend on the landmark. The proposed variant, called locVLAD (location-aware
VLAD), computes the mean of the two global descriptors: the VLAD executed on
the entire original image, and the one computed on a cropped image which
removes a certain percentage of the image borders. This simple variant shows an
accuracy greater than the existing state-of-the-art approach. Experiments are
conducted on two public datasets (ZuBuD and Holidays) which are used both for
training and testing. Morever a more balanced version of ZuBuD is proposed.Comment: 6 pages, 5 figures, ICDSC 201
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