Visual object categorization with new keypoint-based adaBoost features

We present promising results for visual object categorization, obtained with adaBoost using new original ?keypoints-based features?. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a ?keypoint? (a kind of SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing the feature. A first experiment was conducted on a public image dataset containing lateral-viewed cars, yielding 95% recall with 95% precision on test set. Preliminary tests on a small subset of a pedestrians database also gives promising 97% recall with 92 % precision, which shows the generality of our new family of features. Moreover, analysis of the positions of adaBoost-selected keypoints show that they correspond to a specific part of the object category (such as ?wheel? or ?side skirt? in the case of lateral-cars) and thus have a ?semantic? meaning. We also made a first test on video for detecting vehicles from adaBoostselected keypoints filtered in real-time from all detected keypoints

AdaBoost with "keypoint presence features" for real-time vehivle visual detection

International audienceWe present promising results for real-time vehicle visual detection, obtained with adaBoost using new original “keypoints presence features”. These weak-classifiers produce a boolean response based on presence or absence in the tested image of a “keypoint” (~ a SURF interest point) with a descriptor sufficiently similar (i.e. within a given distance) to a reference descriptor characterizing the feature. A first experiment was conducted on a public image dataset containing lateral-viewed cars, yielding 95% recall with 95% precision on test set. Moreover, analysis of the positions of adaBoost-selected keypoints show that they correspond to a specific part of the object category (such as “wheel” or “side skirt”) and thus have a “semantic” meaning

Keypoints-based background model and foreground pedestrian extraction for future smart cameras

International audienceIn this paper, we present a method for background modeling using only keypoints, and detection of foreground moving pedestrians using background keypoints substraction followed by adaBoost classification of foreground keypoints. A first experimental evaluation shows very promising detection performances in real-time

Extraction of Exclusive Video Content from One Shot Video

With the popularity of personal digital devices, the amount of home video data is growing explosively. Many videos may only contain a single shot and are very short and their contents are diverse yet related with few major subjects or events. Users often ne ed to maintain their own video clip collections captured at different locations and time. These unedited and unorganized videos bring difficulties to their management and manipulation. This video composition system is used to generate aesthetically enhanced long - shot videos from short video clips. Our proposed system is to extract the video contents about a specific topic and compose them into a virtual one - shot presentation. All input short video clips are pre - processed and converted as one - shot video. Video frames are detected and categorized by using transition clues like human, object. Human and object frames are separated by implementing a face detection algorithm for the input one - shot video. Viola Jones face detection algorithm is used for separating human and object frames. There are three ingredients in this algorithm, worki ng in concert to enable a fast and a ccurate detection. The integral image for feature computation, adaboost for feature selection and an attentional cascade for efficient computational resource allocation. Objects are then categorized using SIFT (Scale Invariant Feature Transform) and SURF ( Speed Up Robust Features) algorithm

Hausdorff-Distance Enhanced Matching of Scale Invariant Feature Transform Descriptors in Context of Image Querying

Reliable and effective matching of visual descriptors is a key step for many vision applications, e.g. image retrieval. In this paper, we propose to integrate the Hausdorff distance matching together with our pairing algorithm, in order to obtain a robust while computationally efficient process of matching feature descriptors for image-to-image querying in standards datasets. For this purpose, Scale Invariant Feature Transform (SIFT) descriptors have been matched using our presented algorithm, followed by the computation of our related similarity measure. This approach has shown excellent performance in both retrieval accuracy and speed

Feature extraction techniques for abandoned object classification in video surveillance

We address the problem of abandoned object classification in video surveillance. Our aim is to determine (i) which feature extraction technique proves more useful for accurate object classification in a video surveillance context (scale invariant image transform (SIFT) keypoints vs. geometric primitive features), and (ii) how the resulting features affect classification accuracy and false positive rates for different classification schemes used. Objects are classified into four different categories: bag (s), person (s), trolley (s), and group (s) of people. Our experimental results show that the highest recognition accuracy and the lowest false alarm rate are achieved by building a classifier based on our proposed set of statistics of geometric primitives' features. Moreover, classification performance based on this set of features proves to be more invariant across different learning algorithms. © 2008 IEEE

A Review of Codebook Models in Patch-Based Visual Object Recognition

The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods