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This paper proposed a novel approach to the kichiji rockfish recognition method for investigation of the fish biomass. This method does not depend on the size, position and attitude of the fish, because SIFT feature with rotation invariant is used and is represented by vector quantization histogram. The experiment results showed that our method detected 94 % of object which is the fish from 37 photographs...
This paper presents a novel encoding method for scene change detection and appearance-based topological localization framework. The relation computation over convex hull points is used to compare the similarity between the scenes. It relies on the relative ordering of the feature strength, not directly on the feature vectors. We first deal with multiple convex hulls over the detected features and...
This paper proposes an efficient approach for object classification. This method bases on bag-of-features classification framework and extends the limits of it. It applies modified spatial PACT as local feature descriptor, which can efficiently catch image patch's characteristic. In order to address the speed bottleneck of codebook creation, extremely randomized clustering forest is used to create...
In shot boundary detection, the key technology is to compute the visual content discontinuity values between consecutive video frames. In this paper, a unified framework is proposed to detect the shot boundaries and extract the keyframes of a shot. Firstly, the scale invariant feature transform (SIFT) is adopted to compute the visual content discontinuity values. Then a new method, which is called...
Acoustic events produced in meeting environments may contain useful information for perceptually aware interfaces and multimodal behavior analysis. In this paper, a system to detect and recognize these events from a multimodal perspective is presented combining information from multiple cameras and microphones. First, spectral and temporal features are extracted from a single audio channel and spatial...
The problem of automatic object categorization is investigated under the proposed bag of feature object categorization framework. The framework consists of feature detection and representation which uses the scale invariant feature transform (SIFT) as local feature and bag of feature model to represent the image. Learning process utilizes k-NN (k-nearest neighbour). In this paper, we propose the dimensionality...
The paper presents fundamentals and preliminary results of a technique for defining, building and positioning novel local feature. The features are created by approximating the content of a scanning circular window by a collection of predefined patterns. Although basics of the technique have been discussed in previous papers, the major modification is the introduction of Hough transform as a part...
Recent work in visual retrieval shows that bag-of-features (BoF) has appeared promising for object recognition and categorization. Local descriptors such as SIFT have shown impressive results on objects. The main idea of BoF is to depict each image as an orderless collection of local keypoint features. However, not all the local keypoint features are important for retrieving objects and rather, the...
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