16763535
Handling Significant Scale Difference for Object Retrieval in a Supermarket
We propose an object retrieval application which can retrieve user specified objects from a big supermarket. Significant and unpredictable scale difference between the query and the database image is the major obstacle encountered. The widely used local invariant features show their deficiency in such an occasion. To improve the situation, we first design a new weighting scheme which can assess the repeatability of local features against scale variance. Also, another method which deals with scale difference through retrieving a query under multiple scales is also developed. Our methods have been tested on a real image database collected from a local supermarket and outperform the existing local invariant feature based image retrieval approaches. A new spatial check method is also briefly discussed.
2370296
Regularized Discriminative Direction for Shape Difference Analysis
The "discriminative direction" has been proven useful to reveal the subtle difference between two anatomical shape classes. When a shape moves along this direction, its deformation will best manifest the class difference detected by a kernel classifier. However, we observe that such a direction cannot maintain a shape's "anatomical" correctness, introducing spurious difference. To overcome this drawback, we develop a regularized discriminative direction by requiring a shape to conform to its population distribution when it deforms along the discriminative direction. Instead of iterative optimization, an analytic solution is provided to directly work out this direction. Experimental study shows its superior performance in detecting and localizing the difference of hippocampal shapes for sex. The result is supported by other independent research in the same domain.
2447873
Multilabel SVM active learning for image classification
Image classification is an important task in computer vision. However, how to assign suitable labels to images is a subjective matter, especially when some images can be categorized into multiple classes simultaneously. Multilabel image classification focuses on the problem that each image can have one or multiple labels. It is known that manually labelling images is time-consuming and expensive. In order to reduce the human effort of labelling images, especially multilabel images, we proposed a multilabel SVM active learning method. We also proposed two selection strategies: Max Loss strategy and Mean Max Loss strategy. Experimental results on both artificial data and real-world images demonstrated the advantage of proposed method.
7983053
Auto-Calibration of a Compound-Type Omnidirectional Camera
We propose a method for auto-calibrating a compound-type omnidirectional camera (system). This camera produces spherical panoramic image through stitching some individual images captured by several modular cameras, each of which covers a different field of view of the full viewing sphere. By auto-calibration we mean that our algorithm works online in a self-calibration manner: it receives as input image point correspondences only, and outputs all the camera parameters. The calibration task consists of lens-distortion-removal, intrinsic-calibration, and relative-orientation. The focus of the paper is an algorithm for automatically removing lens distortion. By exploiting planar projective invariants, our algorithm is capable of estimating centre-of-distortion and distortion parameters efficiently without using any nonlinear iteration.
17470669
Retrieval with knowledge-driven kernel design: an approach to improving SVM-based CBIR with relevance feedback
The performance of SVM-based image retrieval is often constrained by the scarcity of training samples. The total number of image samples labeled by users in a retrieval session is very limited, and this small number of labeled samples cannot effectively represent the true distributions of positive and negative image classes, especially for the negative image class. This paper proposes a novel approach to deal with this problem. Instead of treating it as a problem, the mere existence of the small number of labeled images and their desired distribution in the kernel space is considered as prior knowledge from image retrieval to aid the design of the kernel used by SVMs. This is achieved by maximizing a criterion, such as one based on scatter matrices, through gradient-based search methods, incurring very little computational overhead to real-time retrieval process. Experimental results on two benchmark image databases demonstrate the improved retrieval performance by the dynamically designed kernel and hence the effectiveness of the proposed approach for SVM based image retrieval
16543010
A novel multi-resolution video representation scheme based on kernel PCA
Content-based video analysis calls for efficient and flexible video representation. In this paper, a novel multi-resolution video representation (MRVR) scheme is proposed and realized by performing the kernel principal component analysis (KPCA) on the low-level visual features extracted from a video sequence. By simply changing the kernel parameters or the dimensionality of the subspace, this scheme can represent video content from coarser to finer levels in the subspace, according to its intrinsic structure. An application of keyframe extraction is investigated to show the advantages of this representation scheme. Furthermore, based on this scheme, a two-level video summarization approach is proposed to represent long video sequences. The experimental results of both short and long video sequences have demonstrated the effectiveness and flexibility of the proposed video representation scheme.
8484163
Near-Collision Attacks on MD4: Applied to MD4-Based Protocols
The most widely used hash functions from MD4 family have been broken, which lead to a public competition on designing new hash functions held by NIST. This paper focuses on one concept called near-collision resistance: computationally difficult to find a pair of messages with hash values differing in only few bits, which new hash functions should satisfy. In this paper, we will give a model of near-collisions on MD4, and apply it to attack protocols including HMAC/NMAC-MD4 and MD4(Password||Challenge). Our new outer-key recovery attacks on HMAC/NMAC-MD4 has a complexity of 272 online queries and 277 MD4 computations, while previous result was 288 online queries and 295 MD4 computations. Our attack on MD4(Password||Challenge) can recover 16 password characters with a complexity of 237 online queries and 221 MD4 computations, which is the first approach to attack such protocols.
6828357
Study on Traffic Information Fusion Algorithm Based on Support Vector Machines
Support vector machine (SVM) is a new sort of machine learning method based on structure risk minimization (SRM) principle, which has high generalization capability. Many problems with small samples, nonlinearity or high dimension in pattern recognition could be solved by the method. In this paper, the traffic data on freeway were taken as research objects and an information fusion algorithm based on SVM about freeway incident detection was proposed. A SVM was trained and tested using the data obtained from the simulation under the condition of incident and non-incident. Compared with the multi-layer feed forward neural network (MLF) algorithm trained with the same data, the simulation results showed that the SVM offers a lower misclassification rate, higher correct detection rate and lower false alarm, and it can improve the detection performance
32712174
Hailstone Detection Based on Image Mining
Hailstone is one of the main meteorological disasters and it is very difficult to be forecasted effectively. A novel method of automatic recognition hail cloud is proposed base on association rules in this paper. Firstly, the method preprocesses the original reflectivity image. Then the color features and texture features are extracted from the radar echo image. Lastly, the association rules, which are used to distinguish hailstorm from rainstorm and super refract, are constructed, namely hail echo ensemble forecast model based on image mining is constructed. The results show that hail echo ensemble forecast model based on image mining has higher accuracy compared with the PUP products.
29918508
Round-Robin with VirtualClock Scheduling Algorithm in Multiservice Packet Networks
In this paper, we present a scheduler that incorporates round robin service within a VirtualClock discipline. Time-stamp based scheduling algorithms attain a low local delay bound and performance guarantee, but are computationally complex. On the other hand, round robin schemes are simple to implement and have computational complexity of O(1) but they are well known for their output burstiness and short-term unfairness. In order to overcome this problem, we combine round robin with VirtualClock in an algorithm we call VCRR. VCRR possesses better fairness than simple round robin, low jitter and a good scheduling delay bound. At the same time, VCRR preserves the O(1) time complexity of round robin. Simulation experiments show VCRR's efficiency in terms of delay performance, jitter and fairness..
633050
Query by humming via multiscale transportation distance in random query occurrence context
Query by humming (QBH) is an interactive tool for retrieving favored songs from a large database of known media via acoustic input. In this task, common method for measuring similarity between query and candidate is either by symbolic notation distance or by framed based dynamic programming. However, the former has disadvantage of error-prone to the noted symbolic feature extraction stage, while the latter is time-consuming. It has been proved that transportation distance has its remarkable merit in image query. However, we adopt a new structure for handling QBH, which is based on an improved version of this measure in combination with a string searching algorithm. More practically, we extend this method in random piece context, which means users can hum at any part of music piece. Experimental results are evaluated in MIREX 2007. Final 92.82% MRR has shown its significant advantages.