Classification using intersection kernel support vector machines is efficient

Maji, Subhransu; Berg, Alexander C.; Malik, Jitendra

doi:10.1109/cvpr.2008.4587630

Cited by 813 publications

(541 citation statements)

References 11 publications

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“…• We learn an Intersection Kernel Support Vector Machine (IKSVM [16,22]) based on the positive and negative sets for each component, and the model is bootstrapped once by data-mining hard negatives. The SVM scores are then converted to probabilities through a sigmoid function whose parameters are also learned from data.…”

Section: Training Componentsmentioning

confidence: 99%

Multi-component Models for Object Detection

Arbeláez

Lin

et al. 2012

Computer Vision – ECCV 2012

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Abstract. In this paper, we propose a multi-component approach for object detection. Rather than attempting to represent an object category with a monolithic model, or pre-defining a reduced set of aspects, we form visual clusters from the data that are tight in appearance and configuration spaces. We train individual classifiers for each component, and then learn a second classifier that operates at the category level by aggregating responses from multiple components. In order to reduce computation cost during detection, we adopt the idea of object window selection, and our segmentation-based selection mechanism produces fewer than 500 windows per image while preserving high object recall. When compared to the leading methods in the challenging VOC PASCAL 2010 dataset, our multi-component approach obtains highly competitive results. Furthermore, unlike monolithic detection methods, our approach allows the transfer of finer-grained semantic information from the components, such as keypoint location and segmentation masks.

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Section: Training Componentsmentioning

confidence: 99%

Multi-component Models for Object Detection

Arbeláez

Lin

et al. 2012

Computer Vision – ECCV 2012

Self Cite

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“…Since we need a binary categorization, we use SVM in binary classification setting with linear kernel, which is efficient to learn and evaluate compared to nonlinear ones. Also with sophisticated features [26,27] the accuracy of linear kernel is comparable with the accuracy of non linear kernels. Once the hyperplane W for the query q is given, the classification score for any sample X is computed as follows.…”

Section: Retrieval With Eigen Queriesmentioning

confidence: 58%

Image Retrieval Using Eigen Queries

Raval

Tonge

Jawahar

2013

Computer Vision – ACCV 2012

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Abstract. Category based image search, where the goal is to retrieve images of a specific category from a large database, is becoming increasingly popular. In such a setting, the query is often a classifier. However, the complexity of the classifiers (often SVMs) used for this purpose hinders the use of such a solution in practice. Problem becomes paramount when the database is huge and/or the dimensionality of the feature representation is also very large. In this paper, we address this issue by proposing a novel method which decomposes the query classifier into set of known eigen queries. We use their precomputed results (or scores) for computing the ranked list corresponding to novel queries. We also propose an approximate algorithm which accesses only a fraction of the data to perform fast retrieval. Experiments on various datasets show that our method reports high accuracy and efficiency. Apart from retrieval, the proposed method can also be used to discover interesting new concepts from the given dataset.

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“…To handle imbalance in the number of positive versus negative training examples, we fixed the weights of the positive and negative classes by estimating the prior probabilities of the classes on training data. We used the histogram intersection kernel and its efficient approximation as suggested by Maji et al [30]. For difference coded BOWs, we used a linear kernel [19].…”

Section: Visual Featuresmentioning

confidence: 99%

Evaluating multimedia features and fusion for example-based event detection

Myers

Nallapati

Hout

et al. 2013

Machine Vision and Applications

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Multimedia event detection (MED) is a challenging problem because of the heterogeneous content and variable quality found in large collections of Internet videos. To study the value of multimedia features and fusion for representing and learning events from a set of example video clips, we created SESAME, a system for video SEarch with Speed and Accuracy for Multimedia Events. SESAME includes multiple bag-of-words event classifiers based on single data types: low-level visual, motion, and audio features; highlevel semantic visual concepts; and automatic speech recognition. Event detection performance was evaluated for each event classifier. The performance of low-level visual and motion features was improved by the use of difference coding. The accuracy of the visual concepts was nearly as strong as that of the low-level visual features. Experiments with a number of fusion methods for combining the event detection scores from these classifiers revealed that simple fusion methods, such as arithmetic mean, perform as well as or better than other, more complex fusion methods. SESAME's

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Classification using intersection kernel support vector machines is efficient

Cited by 813 publications

References 11 publications

Multi-component Models for Object Detection

Multi-component Models for Object Detection

Image Retrieval Using Eigen Queries

Evaluating multimedia features and fusion for example-based event detection

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